{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QUANWN3rpfC9"
   },
   "source": [
    "# 0. Setup Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "146BB11JpfDA"
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "42hJEdo_pfDB"
   },
   "outputs": [],
   "source": [
    "CUSTOM_MODEL_NAME = 'my_ssd_mobnet' \n",
    "PRETRAINED_MODEL_NAME = 'ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8'\n",
    "PRETRAINED_MODEL_URL = 'http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz'\n",
    "TF_RECORD_SCRIPT_NAME = 'generate_tfrecord.py'\n",
    "LABEL_MAP_NAME = 'label_map.pbtxt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "hbPhYVy_pfDB"
   },
   "outputs": [],
   "source": [
    "paths = {\n",
    "    'WORKSPACE_PATH': os.path.join('Tensorflow', 'workspace'),\n",
    "    'SCRIPTS_PATH': os.path.join('Tensorflow','scripts'),\n",
    "    'APIMODEL_PATH': os.path.join('Tensorflow','models'),\n",
    "    'ANNOTATION_PATH': os.path.join('Tensorflow', 'workspace','annotations'),\n",
    "    'IMAGE_PATH': os.path.join('Tensorflow', 'workspace','images'),\n",
    "    'MODEL_PATH': os.path.join('Tensorflow', 'workspace','models'),\n",
    "    'PRETRAINED_MODEL_PATH': os.path.join('Tensorflow', 'workspace','pre-trained-models'),\n",
    "    'CHECKPOINT_PATH': os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME), \n",
    "    'OUTPUT_PATH': os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME, 'export'), \n",
    "    'TFJS_PATH':os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME, 'tfjsexport'), \n",
    "    'TFLITE_PATH':os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME, 'tfliteexport'), \n",
    "    'PROTOC_PATH':os.path.join('Tensorflow','protoc')\n",
    " }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "LwhWZMI0pfDC"
   },
   "outputs": [],
   "source": [
    "files = {\n",
    "    'PIPELINE_CONFIG':os.path.join('Tensorflow', 'workspace','models', CUSTOM_MODEL_NAME, 'pipeline.config'),\n",
    "    'TF_RECORD_SCRIPT': os.path.join(paths['SCRIPTS_PATH'], TF_RECORD_SCRIPT_NAME), \n",
    "    'LABELMAP': os.path.join(paths['ANNOTATION_PATH'], LABEL_MAP_NAME)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "HR-TfDGrpfDC"
   },
   "outputs": [],
   "source": [
    "for path in paths.values():\n",
    "    if not os.path.exists(path):\n",
    "        if os.name == 'posix':\n",
    "            !mkdir -p {path}\n",
    "        if os.name == 'nt':\n",
    "            !mkdir {path}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OLU-rs_ipfDE"
   },
   "source": [
    "# 1. Download TF Models Pretrained Models from Tensorflow Model Zoo and Install TFOD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.tensorflow.org/install/source_windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "K-Cmz2edpfDE",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wget in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (3.2)\n"
     ]
    }
   ],
   "source": [
    "if os.name=='nt':\n",
    "    !pip install wget\n",
    "    import wget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "iA1DIq5OpfDE"
   },
   "outputs": [],
   "source": [
    "if not os.path.exists(os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection')):\n",
    "    !git clone https://github.com/tensorflow/models {paths['APIMODEL_PATH']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "rJjMHbnDs3Tv"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% [..............................................] 1468733 / 1468733        1 file(s) moved.\n",
      "        1 file(s) copied.\n",
      "running build\n",
      "running build_py\n",
      "copying object_detection\\protos\\anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\argmax_matcher_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\bipartite_matcher_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\box_coder_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\box_predictor_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\calibration_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\center_net_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\eval_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\faster_rcnn_box_coder_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\faster_rcnn_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\flexible_grid_anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\fpn_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\graph_rewriter_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\grid_anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\hyperparams_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\image_resizer_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\input_reader_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\keypoint_box_coder_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\losses_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\matcher_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\mean_stddev_box_coder_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\model_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\multiscale_anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\optimizer_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\pipeline_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\post_processing_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\preprocessor_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\region_similarity_calculator_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\square_box_coder_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\ssd_anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\ssd_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\string_int_label_map_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\target_assigner_pb2.py -> build\\lib\\object_detection\\protos\n",
      "copying object_detection\\protos\\train_pb2.py -> build\\lib\\object_detection\\protos\n",
      "running egg_info\n",
      "writing object_detection.egg-info\\PKG-INFO\n",
      "writing dependency_links to object_detection.egg-info\\dependency_links.txt\n",
      "writing requirements to object_detection.egg-info\\requires.txt\n",
      "writing top-level names to object_detection.egg-info\\top_level.txt\n",
      "reading manifest file 'object_detection.egg-info\\SOURCES.txt'\n",
      "writing manifest file 'object_detection.egg-info\\SOURCES.txt'\n",
      "running install\n",
      "running bdist_egg\n",
      "running egg_info\n",
      "writing object_detection.egg-info\\PKG-INFO\n",
      "writing dependency_links to object_detection.egg-info\\dependency_links.txt\n",
      "writing requirements to object_detection.egg-info\\requires.txt\n",
      "writing top-level names to object_detection.egg-info\\top_level.txt\n",
      "reading manifest file 'object_detection.egg-info\\SOURCES.txt'\n",
      "writing manifest file 'object_detection.egg-info\\SOURCES.txt'\n",
      "installing library code to build\\bdist.win-amd64\\egg\n",
      "running install_lib\n",
      "running build_py\n",
      "creating build\\bdist.win-amd64\\egg\n",
      "creating build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\build_imagenet_data.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\cifar10.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\dataset_factory.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\dataset_utils.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\download_and_convert_cifar10.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\download_and_convert_flowers.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\download_and_convert_mnist.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\download_and_convert_visualwakewords.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\download_and_convert_visualwakewords_lib.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\flowers.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\imagenet.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\mnist.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\preprocess_imagenet_validation_data.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\process_bounding_boxes.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\visualwakewords.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "copying build\\lib\\datasets\\__init__.py -> build\\bdist.win-amd64\\egg\\datasets\n",
      "creating build\\bdist.win-amd64\\egg\\deployment\n",
      "copying build\\lib\\deployment\\model_deploy.py -> build\\bdist.win-amd64\\egg\\deployment\n",
      "copying build\\lib\\deployment\\model_deploy_test.py -> build\\bdist.win-amd64\\egg\\deployment\n",
      "copying build\\lib\\deployment\\__init__.py -> build\\bdist.win-amd64\\egg\\deployment\n",
      "creating build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\alexnet.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\alexnet_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\cifarnet.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\cyclegan.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\cyclegan_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\dcgan.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\dcgan_test.py -> build\\bdist.win-amd64\\egg\\nets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "zip_safe flag not set; analyzing archive contents...\n",
      "object_detection.core.__pycache__.densepose_ops.cpython-37: module references __file__\n",
      "object_detection.core.__pycache__.preprocessor.cpython-37: module MAY be using inspect.stack\n",
      "object_detection.utils.__pycache__.autoaugment_utils.cpython-37: module MAY be using inspect.stack\n",
      "zip_safe flag not set; analyzing archive contents...\n",
      "zip_safe flag not set; analyzing archive contents...\n",
      "cpuinfo.__pycache__.cpuinfo.cpython-37: module references __file__\n",
      "warning: no files found matching 'LICENSE.txt'\n",
      "zip_safe flag not set; analyzing archive contents...\n",
      "error: numpy 1.20.2 is installed but numpy<1.20.0,>=1.14.3 is required by {'apache-beam'}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "copying build\\lib\\nets\\i3d.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\i3d_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\i3d_utils.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_resnet_v2.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_resnet_v2_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_utils.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_v1.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_v1_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_v2.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_v2_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_v3.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_v3_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_v4.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\inception_v4_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\lenet.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "creating build\\bdist.win-amd64\\egg\\nets\\mobilenet\n",
      "copying build\\lib\\nets\\mobilenet\\conv_blocks.py -> build\\bdist.win-amd64\\egg\\nets\\mobilenet\n",
      "copying build\\lib\\nets\\mobilenet\\mobilenet.py -> build\\bdist.win-amd64\\egg\\nets\\mobilenet\n",
      "copying build\\lib\\nets\\mobilenet\\mobilenet_v2.py -> build\\bdist.win-amd64\\egg\\nets\\mobilenet\n",
      "copying build\\lib\\nets\\mobilenet\\mobilenet_v2_test.py -> build\\bdist.win-amd64\\egg\\nets\\mobilenet\n",
      "copying build\\lib\\nets\\mobilenet\\mobilenet_v3.py -> build\\bdist.win-amd64\\egg\\nets\\mobilenet\n",
      "copying build\\lib\\nets\\mobilenet\\mobilenet_v3_test.py -> build\\bdist.win-amd64\\egg\\nets\\mobilenet\n",
      "copying build\\lib\\nets\\mobilenet\\__init__.py -> build\\bdist.win-amd64\\egg\\nets\\mobilenet\n",
      "copying build\\lib\\nets\\mobilenet_v1.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\mobilenet_v1_eval.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\mobilenet_v1_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\mobilenet_v1_train.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "creating build\\bdist.win-amd64\\egg\\nets\\nasnet\n",
      "copying build\\lib\\nets\\nasnet\\nasnet.py -> build\\bdist.win-amd64\\egg\\nets\\nasnet\n",
      "copying build\\lib\\nets\\nasnet\\nasnet_test.py -> build\\bdist.win-amd64\\egg\\nets\\nasnet\n",
      "copying build\\lib\\nets\\nasnet\\nasnet_utils.py -> build\\bdist.win-amd64\\egg\\nets\\nasnet\n",
      "copying build\\lib\\nets\\nasnet\\nasnet_utils_test.py -> build\\bdist.win-amd64\\egg\\nets\\nasnet\n",
      "copying build\\lib\\nets\\nasnet\\pnasnet.py -> build\\bdist.win-amd64\\egg\\nets\\nasnet\n",
      "copying build\\lib\\nets\\nasnet\\pnasnet_test.py -> build\\bdist.win-amd64\\egg\\nets\\nasnet\n",
      "copying build\\lib\\nets\\nasnet\\__init__.py -> build\\bdist.win-amd64\\egg\\nets\\nasnet\n",
      "copying build\\lib\\nets\\nets_factory.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\nets_factory_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\overfeat.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\overfeat_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\pix2pix.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\pix2pix_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\post_training_quantization.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\resnet_utils.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\resnet_v1.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\resnet_v1_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\resnet_v2.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\resnet_v2_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\s3dg.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\s3dg_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\vgg.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\vgg_test.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "copying build\\lib\\nets\\__init__.py -> build\\bdist.win-amd64\\egg\\nets\n",
      "creating build\\bdist.win-amd64\\egg\\object_detection\n",
      "creating build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\flexible_grid_anchor_generator.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\flexible_grid_anchor_generator_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\grid_anchor_generator.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\grid_anchor_generator_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\multiple_grid_anchor_generator.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\multiple_grid_anchor_generator_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\multiscale_grid_anchor_generator.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\multiscale_grid_anchor_generator_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "copying build\\lib\\object_detection\\anchor_generators\\__init__.py -> build\\bdist.win-amd64\\egg\\object_detection\\anchor_generators\n",
      "creating build\\bdist.win-amd64\\egg\\object_detection\\box_coders\n",
      "copying build\\lib\\object_detection\\box_coders\\faster_rcnn_box_coder.py -> build\\bdist.win-amd64\\egg\\object_detection\\box_coders\n",
      "copying build\\lib\\object_detection\\box_coders\\faster_rcnn_box_coder_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\box_coders\n",
      "copying build\\lib\\object_detection\\box_coders\\keypoint_box_coder.py -> build\\bdist.win-amd64\\egg\\object_detection\\box_coders\n",
      "copying build\\lib\\object_detection\\box_coders\\keypoint_box_coder_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\box_coders\n",
      "copying build\\lib\\object_detection\\box_coders\\mean_stddev_box_coder.py -> build\\bdist.win-amd64\\egg\\object_detection\\box_coders\n",
      "copying build\\lib\\object_detection\\box_coders\\mean_stddev_box_coder_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\box_coders\n",
      "copying build\\lib\\object_detection\\box_coders\\square_box_coder.py -> build\\bdist.win-amd64\\egg\\object_detection\\box_coders\n",
      "copying build\\lib\\object_detection\\box_coders\\square_box_coder_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\box_coders\n",
      "copying build\\lib\\object_detection\\box_coders\\__init__.py -> build\\bdist.win-amd64\\egg\\object_detection\\box_coders\n",
      "creating build\\bdist.win-amd64\\egg\\object_detection\\builders\n",
      "copying build\\lib\\object_detection\\builders\\anchor_generator_builder.py -> build\\bdist.win-amd64\\egg\\object_detection\\builders\n",
      "copying build\\lib\\object_detection\\builders\\anchor_generator_builder_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\builders\n",
      "copying build\\lib\\object_detection\\builders\\box_coder_builder.py -> build\\bdist.win-amd64\\egg\\object_detection\\builders\n",
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      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\np_box_mask_list.py to np_box_mask_list.cpython-37.pyc\n",
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      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\np_box_mask_list_ops_test.py to np_box_mask_list_ops_test.cpython-37.pyc\n",
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      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\np_box_ops.py to np_box_ops.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\np_box_ops_test.py to np_box_ops_test.cpython-37.pyc\n",
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      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\object_detection_evaluation.py to object_detection_evaluation.cpython-37.pyc\n",
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      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\per_image_vrd_evaluation_test.py to per_image_vrd_evaluation_test.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\shape_utils.py to shape_utils.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\shape_utils_test.py to shape_utils_test.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\spatial_transform_ops.py to spatial_transform_ops.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\spatial_transform_ops_test.py to spatial_transform_ops_test.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\static_shape.py to static_shape.cpython-37.pyc\n",
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      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\target_assigner_utils_test.py to target_assigner_utils_test.cpython-37.pyc\n",
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      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\test_case_test.py to test_case_test.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\test_utils.py to test_utils.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\test_utils_test.py to test_utils_test.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\tf_version.py to tf_version.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\variables_helper.py to variables_helper.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\variables_helper_tf1_test.py to variables_helper_tf1_test.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\visualization_utils.py to visualization_utils.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\visualization_utils_test.py to visualization_utils_test.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\vrd_evaluation.py to vrd_evaluation.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\vrd_evaluation_test.py to vrd_evaluation_test.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\utils\\__init__.py to __init__.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\object_detection\\__init__.py to __init__.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\preprocessing\\cifarnet_preprocessing.py to cifarnet_preprocessing.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\preprocessing\\inception_preprocessing.py to inception_preprocessing.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\preprocessing\\lenet_preprocessing.py to lenet_preprocessing.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\preprocessing\\preprocessing_factory.py to preprocessing_factory.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\preprocessing\\vgg_preprocessing.py to vgg_preprocessing.cpython-37.pyc\n",
      "byte-compiling build\\bdist.win-amd64\\egg\\preprocessing\\__init__.py to __init__.cpython-37.pyc\n",
      "creating build\\bdist.win-amd64\\egg\\EGG-INFO\n",
      "copying object_detection.egg-info\\PKG-INFO -> build\\bdist.win-amd64\\egg\\EGG-INFO\n",
      "copying object_detection.egg-info\\SOURCES.txt -> build\\bdist.win-amd64\\egg\\EGG-INFO\n",
      "copying object_detection.egg-info\\dependency_links.txt -> build\\bdist.win-amd64\\egg\\EGG-INFO\n",
      "copying object_detection.egg-info\\requires.txt -> build\\bdist.win-amd64\\egg\\EGG-INFO\n",
      "copying object_detection.egg-info\\top_level.txt -> build\\bdist.win-amd64\\egg\\EGG-INFO\n",
      "creating 'dist\\object_detection-0.1-py3.7.egg' and adding 'build\\bdist.win-amd64\\egg' to it\n",
      "removing 'build\\bdist.win-amd64\\egg' (and everything under it)\n",
      "Processing object_detection-0.1-py3.7.egg\n",
      "removing 'd:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\object_detection-0.1-py3.7.egg' (and everything under it)\n",
      "creating d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\object_detection-0.1-py3.7.egg\n",
      "Extracting object_detection-0.1-py3.7.egg to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "object-detection 0.1 is already the active version in easy-install.pth\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\object_detection-0.1-py3.7.egg\n",
      "Processing dependencies for object-detection==0.1\n",
      "Searching for contextlib2\n",
      "Reading https://pypi.org/simple/contextlib2/\n",
      "Downloading https://files.pythonhosted.org/packages/85/60/370352f7ef6aa96c52fb001831622f50f923c1d575427d021b8ab3311236/contextlib2-0.6.0.post1-py2.py3-none-any.whl#sha256=3355078a159fbb44ee60ea80abd0d87b80b78c248643b49aa6d94673b413609b\n",
      "Best match: contextlib2 0.6.0.post1\n",
      "Processing contextlib2-0.6.0.post1-py2.py3-none-any.whl\n",
      "Installing contextlib2-0.6.0.post1-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "Adding contextlib2 0.6.0.post1 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\contextlib2-0.6.0.post1-py3.7.egg\n",
      "Searching for Cython\n",
      "Reading https://pypi.org/simple/Cython/\n",
      "Downloading https://files.pythonhosted.org/packages/78/36/7014318ffdf61024d78599bf2b1b80284c09c7c0f6efad7480ca83cd434b/Cython-3.0a6-cp37-cp37m-win_amd64.whl#sha256=a18505602d5c2e0f3973b30b3d851c0b99e6f5d75a5447a70f49294611e9e0ce\n",
      "Best match: Cython 3.0a6\n",
      "Processing Cython-3.0a6-cp37-cp37m-win_amd64.whl\n",
      "Installing Cython-3.0a6-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "Adding Cython 3.0a6 to easy-install.pth file\n",
      "Installing cygdb-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing cygdb.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing cython-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing cython.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing cythonize-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing cythonize.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\cython-3.0a6-py3.7-win-amd64.egg\n",
      "Searching for matplotlib\n",
      "Reading https://pypi.org/simple/matplotlib/\n",
      "Downloading https://files.pythonhosted.org/packages/56/3f/0a1b147f23830ca027f9e93cb4985957dbef690649f249063718c6e5c19b/matplotlib-3.4.1-cp37-cp37m-win_amd64.whl#sha256=c45e7bf89ea33a2adaef34774df4e692c7436a18a48bcb0e47a53e698a39fa39\n",
      "Best match: matplotlib 3.4.1\n",
      "Processing matplotlib-3.4.1-cp37-cp37m-win_amd64.whl\n",
      "Installing matplotlib-3.4.1-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\matplotlib-3.4.1-py3.7-win-amd64.egg\\EGG-INFO\\requires.txt\n",
      "Adding matplotlib 3.4.1 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\matplotlib-3.4.1-py3.7-win-amd64.egg\n",
      "Searching for pillow\n",
      "Reading https://pypi.org/simple/pillow/\n",
      "Downloading https://files.pythonhosted.org/packages/84/41/b233abcfb9bae12768bffae92e2c96d2d807cd92a3cef7d24987df0e3d64/Pillow-8.2.0-cp37-cp37m-win_amd64.whl#sha256=3b570f84a6161cf8865c4e08adf629441f56e32f180f7aa4ccbd2e0a5a02cba2\n",
      "Best match: Pillow 8.2.0\n",
      "Processing Pillow-8.2.0-cp37-cp37m-win_amd64.whl\n",
      "Installing Pillow-8.2.0-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "Adding Pillow 8.2.0 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\pillow-8.2.0-py3.7-win-amd64.egg\n",
      "Searching for apache-beam\n",
      "Reading https://pypi.org/simple/apache-beam/\n",
      "Downloading https://files.pythonhosted.org/packages/2d/c9/8a706032b946c5486482d928fe5eb274d39eddfc1ea41e684ce723b760ef/apache_beam-2.28.0-cp37-cp37m-win_amd64.whl#sha256=605c0329bc47c17275ed24cd13a0bfe6fe64fad81588551cd3d3507644c1b09d\n",
      "Best match: apache-beam 2.28.0\n",
      "Processing apache_beam-2.28.0-cp37-cp37m-win_amd64.whl\n",
      "Installing apache_beam-2.28.0-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\apache_beam-2.28.0-py3.7-win-amd64.egg\\EGG-INFO\\requires.txt\n",
      "Adding apache-beam 2.28.0 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\apache_beam-2.28.0-py3.7-win-amd64.egg\n",
      "Searching for avro-python3\n",
      "Reading https://pypi.org/simple/avro-python3/\n",
      "Downloading https://files.pythonhosted.org/packages/cc/97/7a6970380ca8db9139a3cc0b0e3e0dd3e4bc584fb3644e1d06e71e1a55f0/avro-python3-1.10.2.tar.gz#sha256=3b63f24e6b04368c3e4a6f923f484be0230d821aad65ac36108edbff29e9aaab\n",
      "Best match: avro-python3 1.10.2\n",
      "Processing avro-python3-1.10.2.tar.gz\n",
      "Writing C:\\Users\\User\\AppData\\Local\\Temp\\easy_install-o0qh6aes\\avro-python3-1.10.2\\setup.cfg\n",
      "Running avro-python3-1.10.2\\setup.py -q bdist_egg --dist-dir C:\\Users\\User\\AppData\\Local\\Temp\\easy_install-o0qh6aes\\avro-python3-1.10.2\\egg-dist-tmp-u9g8xvl0\n",
      "Copying avro_python3-1.10.2-py3.7.egg to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "Adding avro-python3 1.10.2 to easy-install.pth file\n",
      "Installing avro script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\avro_python3-1.10.2-py3.7.egg\n",
      "Searching for tensorflow>=2.4.0\n",
      "Reading https://pypi.org/simple/tensorflow/\n",
      "Downloading https://files.pythonhosted.org/packages/ab/7a/eebd927c1f10f97a51d8276d933b0459bd0d705920c3095ec433bf529d33/tensorflow-2.5.0rc0-cp37-cp37m-win_amd64.whl#sha256=48738c7eb1262a13b50df1ff6860c9fa056ae942c7b29bb5c6cc78a025862840\n",
      "Best match: tensorflow 2.5.0rc0\n",
      "Processing tensorflow-2.5.0rc0-cp37-cp37m-win_amd64.whl\n",
      "Installing tensorflow-2.5.0rc0-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow-2.5.0rc0-py3.7-win-amd64.egg\\EGG-INFO\\requires.txt\n",
      "Adding tensorflow 2.5.0rc0 to easy-install.pth file\n",
      "Installing estimator_ckpt_converter-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing estimator_ckpt_converter.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing import_pb_to_tensorboard-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing import_pb_to_tensorboard.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing saved_model_cli-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing saved_model_cli.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing tensorboard-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing tensorboard.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing tf_upgrade_v2-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing tf_upgrade_v2.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing tflite_convert-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing tflite_convert.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing toco-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing toco.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing toco_from_protos-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing toco_from_protos.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow-2.5.0rc0-py3.7-win-amd64.egg\n",
      "Searching for tensorflow-model-optimization>=0.4.1\n",
      "Reading https://pypi.org/simple/tensorflow-model-optimization/\n",
      "Downloading https://files.pythonhosted.org/packages/55/38/4fd48ea1bfcb0b6e36d949025200426fe9c3a8bfae029f0973d85518fa5a/tensorflow_model_optimization-0.5.0-py2.py3-none-any.whl#sha256=06db9b86d871e1dcc40a90515cf0a0f3228ac71cac3f4c49b5c2aef96f45852b\n",
      "Best match: tensorflow-model-optimization 0.5.0\n",
      "Processing tensorflow_model_optimization-0.5.0-py2.py3-none-any.whl\n",
      "Installing tensorflow_model_optimization-0.5.0-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow_model_optimization-0.5.0-py3.7.egg\\EGG-INFO\\requires.txt\n",
      "Adding tensorflow-model-optimization 0.5.0 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow_model_optimization-0.5.0-py3.7.egg\n",
      "Searching for tensorflow-hub>=0.6.0\n",
      "Reading https://pypi.org/simple/tensorflow-hub/\n",
      "Downloading https://files.pythonhosted.org/packages/f4/1f/ca72fa5400954903aa6f3354142cd66374604384fb13429af9e41b12120d/tensorflow_hub-0.11.0-py2.py3-none-any.whl#sha256=19399a8abef10682b4f739a5aa78b43da3937df17f5d2afb0547945798787674\n",
      "Best match: tensorflow-hub 0.11.0\n",
      "Processing tensorflow_hub-0.11.0-py2.py3-none-any.whl\n",
      "Installing tensorflow_hub-0.11.0-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow_hub-0.11.0-py3.7.egg\\EGG-INFO\\requires.txt\n",
      "Adding tensorflow-hub 0.11.0 to easy-install.pth file\n",
      "Installing make_image_classifier-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing make_image_classifier.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing make_nearest_neighbour_index-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing make_nearest_neighbour_index.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow_hub-0.11.0-py3.7.egg\n",
      "Searching for tensorflow-datasets\n",
      "Reading https://pypi.org/simple/tensorflow-datasets/\n",
      "Downloading https://files.pythonhosted.org/packages/d3/91/c3e36695ca04e6f3c2d920887d7dc36550f6bbb03d7d5fd03c2172b06d97/tensorflow_datasets-4.2.0-py3-none-any.whl#sha256=be2532e7de3c7aac3e95a094c3c95b9b1f1a7f2e98daeb16d212dda4382de264\n",
      "Best match: tensorflow-datasets 4.2.0\n",
      "Processing tensorflow_datasets-4.2.0-py3-none-any.whl\n",
      "Installing tensorflow_datasets-4.2.0-py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow_datasets-4.2.0-py3.7.egg\\EGG-INFO\\requires.txt\n",
      "Adding tensorflow-datasets 4.2.0 to easy-install.pth file\n",
      "Installing tfds-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing tfds.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow_datasets-4.2.0-py3.7.egg\n",
      "Searching for tensorflow-addons\n",
      "Reading https://pypi.org/simple/tensorflow-addons/\n",
      "Downloading https://files.pythonhosted.org/packages/0f/87/56e435b4cd3c23e6aeaf438f9f517f08ac406ae7a0d6615e42295280eb7b/tensorflow_addons-0.12.1-cp37-cp37m-win_amd64.whl#sha256=259233e45494354b25c1e45581f0ff7a99e50a38256b353b25113f971c4d364b\n",
      "Best match: tensorflow-addons 0.12.1\n",
      "Processing tensorflow_addons-0.12.1-cp37-cp37m-win_amd64.whl\n",
      "Installing tensorflow_addons-0.12.1-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow_addons-0.12.1-py3.7-win-amd64.egg\\EGG-INFO\\requires.txt\n",
      "Adding tensorflow-addons 0.12.1 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow_addons-0.12.1-py3.7-win-amd64.egg\n",
      "Searching for seqeval\n",
      "Reading https://pypi.org/simple/seqeval/\n",
      "Downloading https://files.pythonhosted.org/packages/9d/2d/233c79d5b4e5ab1dbf111242299153f3caddddbb691219f363ad55ce783d/seqeval-1.2.2.tar.gz#sha256=f28e97c3ab96d6fcd32b648f6438ff2e09cfba87f05939da9b3970713ec56e6f\n",
      "Best match: seqeval 1.2.2\n",
      "Processing seqeval-1.2.2.tar.gz\n",
      "Writing C:\\Users\\User\\AppData\\Local\\Temp\\easy_install-t9lsj_fq\\seqeval-1.2.2\\setup.cfg\n",
      "Running seqeval-1.2.2\\setup.py -q bdist_egg --dist-dir C:\\Users\\User\\AppData\\Local\\Temp\\easy_install-t9lsj_fq\\seqeval-1.2.2\\egg-dist-tmp-dopodyzy\n",
      "Copying seqeval-1.2.2-py3.7.egg to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "Adding seqeval 1.2.2 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\seqeval-1.2.2-py3.7.egg\n",
      "Searching for sentencepiece\n",
      "Reading https://pypi.org/simple/sentencepiece/\n",
      "Downloading https://files.pythonhosted.org/packages/2b/6d/b34d43118887d514f19fea262c09ed1fc357f86a2c42d71fe46a84818e55/sentencepiece-0.1.95-cp37-cp37m-win_amd64.whl#sha256=c2add7d87c30898661de5b9e492bd99c5b184c731dec3c7dd3d2c956e4003446\n",
      "Best match: sentencepiece 0.1.95\n",
      "Processing sentencepiece-0.1.95-cp37-cp37m-win_amd64.whl\n",
      "Installing sentencepiece-0.1.95-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "Adding sentencepiece 0.1.95 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\sentencepiece-0.1.95-py3.7-win-amd64.egg\n",
      "Searching for pyyaml>=5.1\n",
      "Reading https://pypi.org/simple/pyyaml/\n",
      "Downloading https://files.pythonhosted.org/packages/66/7a/9f3a49dea61e98ac8bd3b6b619da4fcb59053232a6e11e9e865dab7c16e0/PyYAML-5.4.1-cp37-cp37m-win_amd64.whl#sha256=08682f6b72c722394747bddaf0aa62277e02557c0fd1c42cb853016a38f8dedf\n",
      "Best match: PyYAML 5.4.1\n",
      "Processing PyYAML-5.4.1-cp37-cp37m-win_amd64.whl\n",
      "Installing PyYAML-5.4.1-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "Adding PyYAML 5.4.1 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\pyyaml-5.4.1-py3.7-win-amd64.egg\n",
      "Searching for py-cpuinfo>=3.3.0\n",
      "Reading https://pypi.org/simple/py-cpuinfo/\n",
      "Downloading https://files.pythonhosted.org/packages/f6/f5/8e6e85ce2e9f6e05040cf0d4e26f43a4718bcc4bce988b433276d4b1a5c1/py-cpuinfo-7.0.0.tar.gz#sha256=9aa2e49675114959697d25cf57fec41c29b55887bff3bc4809b44ac6f5730097\n",
      "Best match: py-cpuinfo 7.0.0\n",
      "Processing py-cpuinfo-7.0.0.tar.gz\n",
      "Writing C:\\Users\\User\\AppData\\Local\\Temp\\easy_install-vtzj0arj\\py-cpuinfo-7.0.0\\setup.cfg\n",
      "Running py-cpuinfo-7.0.0\\setup.py -q bdist_egg --dist-dir C:\\Users\\User\\AppData\\Local\\Temp\\easy_install-vtzj0arj\\py-cpuinfo-7.0.0\\egg-dist-tmp-_xlgbztg\n",
      "creating d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\py_cpuinfo-7.0.0-py3.7.egg\n",
      "Extracting py_cpuinfo-7.0.0-py3.7.egg to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "Adding py-cpuinfo 7.0.0 to easy-install.pth file\n",
      "Installing cpuinfo-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing cpuinfo.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\py_cpuinfo-7.0.0-py3.7.egg\n",
      "Searching for psutil>=5.4.3\n",
      "Reading https://pypi.org/simple/psutil/\n",
      "Downloading https://files.pythonhosted.org/packages/71/ce/35107e81e7eae55c847313f872d4258a71d2640fa04f57c5520fc81473ce/psutil-5.8.0-cp37-cp37m-win_amd64.whl#sha256=fcc01e900c1d7bee2a37e5d6e4f9194760a93597c97fee89c4ae51701de03563\n",
      "Best match: psutil 5.8.0\n",
      "Processing psutil-5.8.0-cp37-cp37m-win_amd64.whl\n",
      "Installing psutil-5.8.0-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\psutil-5.8.0-py3.7-win-amd64.egg\\EGG-INFO\\requires.txt\n",
      "Adding psutil 5.8.0 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\psutil-5.8.0-py3.7-win-amd64.egg\n",
      "Searching for opencv-python-headless\n",
      "Reading https://pypi.org/simple/opencv-python-headless/\n",
      "Downloading https://files.pythonhosted.org/packages/7f/4d/4a6353f32552be7f36c0e7179275718d30c3287c9c4ab4d1ba99e26a0948/opencv_python_headless-4.5.1.48-cp37-cp37m-win_amd64.whl#sha256=fe02a943b1a28b505e954fbce24e867119a3eb4351f93adad55c6cfe81a70484\n",
      "Best match: opencv-python-headless 4.5.1.48\n",
      "Processing opencv_python_headless-4.5.1.48-cp37-cp37m-win_amd64.whl\n",
      "Installing opencv_python_headless-4.5.1.48-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\opencv_python_headless-4.5.1.48-py3.7-win-amd64.egg\\EGG-INFO\\requires.txt\n",
      "Adding opencv-python-headless 4.5.1.48 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\opencv_python_headless-4.5.1.48-py3.7-win-amd64.egg\n",
      "Searching for oauth2client\n",
      "Reading https://pypi.org/simple/oauth2client/\n",
      "Downloading https://files.pythonhosted.org/packages/95/a9/4f25a14d23f0786b64875b91784607c2277eff25d48f915e39ff0cff505a/oauth2client-4.1.3-py2.py3-none-any.whl#sha256=b8a81cc5d60e2d364f0b1b98f958dbd472887acaf1a5b05e21c28c31a2d6d3ac\n",
      "Best match: oauth2client 4.1.3\n",
      "Processing oauth2client-4.1.3-py2.py3-none-any.whl\n",
      "Installing oauth2client-4.1.3-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\oauth2client-4.1.3-py3.7.egg\\EGG-INFO\\requires.txt\n",
      "Adding oauth2client 4.1.3 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\oauth2client-4.1.3-py3.7.egg\n",
      "Searching for kaggle>=1.3.9\n",
      "Reading https://pypi.org/simple/kaggle/\n",
      "Downloading https://files.pythonhosted.org/packages/3a/e7/3bac01547d2ed3d308ac92a0878fbdb0ed0f3d41fb1906c319ccbba1bfbc/kaggle-1.5.12.tar.gz#sha256=b4d87d107bff743aaa805c2b382c3661c4c175cdb159656d4972be2a9cef42cb\n",
      "Best match: kaggle 1.5.12\n",
      "Processing kaggle-1.5.12.tar.gz\n",
      "Writing C:\\Users\\User\\AppData\\Local\\Temp\\easy_install-0zog1127\\kaggle-1.5.12\\setup.cfg\n",
      "Running kaggle-1.5.12\\setup.py -q bdist_egg --dist-dir C:\\Users\\User\\AppData\\Local\\Temp\\easy_install-0zog1127\\kaggle-1.5.12\\egg-dist-tmp-h924dpw_\n",
      "Copying kaggle-1.5.12-py3.7.egg to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "Adding kaggle 1.5.12 to easy-install.pth file\n",
      "Installing kaggle-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing kaggle.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\kaggle-1.5.12-py3.7.egg\n",
      "Searching for google-cloud-bigquery>=0.31.0\n",
      "Reading https://pypi.org/simple/google-cloud-bigquery/\n",
      "Downloading https://files.pythonhosted.org/packages/f7/c1/510fbde69f8ca9d0253326230fe1c36819f94099532312f1174e870f1112/google_cloud_bigquery-2.13.1-py2.py3-none-any.whl#sha256=e0cc41fd5d53d59abf6b87e6d7d9863746c2ee6f136def670938dfb4ee5a4ba3\n",
      "Best match: google-cloud-bigquery 2.13.1\n",
      "Processing google_cloud_bigquery-2.13.1-py2.py3-none-any.whl\n",
      "Installing google_cloud_bigquery-2.13.1-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\google_cloud_bigquery-2.13.1-py3.7.egg\\EGG-INFO\\requires.txt\n",
      "Adding google-cloud-bigquery 2.13.1 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\google_cloud_bigquery-2.13.1-py3.7.egg\n",
      "Searching for google-api-python-client>=1.6.7\n",
      "Reading https://pypi.org/simple/google-api-python-client/\n",
      "Downloading https://files.pythonhosted.org/packages/94/f8/49f913ebb8b8d142a55ae398192704297a4f37d7ec879eb3b6b38f6342fd/google_api_python_client-2.1.0-py2.py3-none-any.whl#sha256=921fe10cdff22d1f5b8af7473f9a298efb991fb6ea67dadd6c37fbecfb0575f4\n",
      "Best match: google-api-python-client 2.1.0\n",
      "Processing google_api_python_client-2.1.0-py2.py3-none-any.whl\n",
      "Installing google_api_python_client-2.1.0-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\google_api_python_client-2.1.0-py3.7.egg\\EGG-INFO\\requires.txt\n",
      "Adding google-api-python-client 2.1.0 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\google_api_python_client-2.1.0-py3.7.egg\n",
      "Searching for gin-config\n",
      "Reading https://pypi.org/simple/gin-config/\n",
      "Downloading https://files.pythonhosted.org/packages/d7/b1/c4950247902f430376c7d9c8a7811269150e66e0207b74330de181dd6779/gin_config-0.4.0-py2.py3-none-any.whl#sha256=774aa7855cd7eca839e796e394159a96060660296b3ca9fbbbf9af427fd152ba\n",
      "Best match: gin-config 0.4.0\n",
      "Processing gin_config-0.4.0-py2.py3-none-any.whl\n",
      "Installing gin_config-0.4.0-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\gin_config-0.4.0-py3.7.egg\\EGG-INFO\\requires.txt\n",
      "Adding gin-config 0.4.0 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\gin_config-0.4.0-py3.7.egg\n",
      "Searching for dataclasses\n",
      "Reading https://pypi.org/simple/dataclasses/\n",
      "Downloading https://files.pythonhosted.org/packages/fe/ca/75fac5856ab5cfa51bbbcefa250182e50441074fdc3f803f6e76451fab43/dataclasses-0.8-py3-none-any.whl#sha256=0201d89fa866f68c8ebd9d08ee6ff50c0b255f8ec63a71c16fda7af82bb887bf\n",
      "Best match: dataclasses 0.8\n",
      "Processing dataclasses-0.8-py3-none-any.whl\n",
      "Installing dataclasses-0.8-py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "Adding dataclasses 0.8 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\dataclasses-0.8-py3.7.egg\n",
      "Searching for pytz>=2017.3\n",
      "Reading https://pypi.org/simple/pytz/\n",
      "Downloading https://files.pythonhosted.org/packages/70/94/784178ca5dd892a98f113cdd923372024dc04b8d40abe77ca76b5fb90ca6/pytz-2021.1-py2.py3-none-any.whl#sha256=eb10ce3e7736052ed3623d49975ce333bcd712c7bb19a58b9e2089d4057d0798\n",
      "Best match: pytz 2021.1\n",
      "Processing pytz-2021.1-py2.py3-none-any.whl\n",
      "Installing pytz-2021.1-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "Adding pytz 2021.1 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\pytz-2021.1-py3.7.egg\n",
      "Searching for pyparsing>=2.4.0\n",
      "Reading https://pypi.org/simple/pyparsing/\n",
      "Downloading https://files.pythonhosted.org/packages/ff/c5/14a5edf227544a6da3cb99a1481fc894db9a81306c9a18b736d3ac9fe8d5/pyparsing-3.0.0b2-py3-none-any.whl#sha256=f4896b4cc085a1f8f8ae53a1a90db5a86b3825ff73eb974dffee3d9e701007f4\n",
      "Best match: pyparsing 3.0.0b2\n",
      "Processing pyparsing-3.0.0b2-py3-none-any.whl\n",
      "Installing pyparsing-3.0.0b2-py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\pyparsing-3.0.0b2-py3.7.egg\\EGG-INFO\\requires.txt\n",
      "Adding pyparsing 3.0.0b2 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\pyparsing-3.0.0b2-py3.7.egg\n",
      "Searching for kiwisolver>=1.1.0\n",
      "Reading https://pypi.org/simple/kiwisolver/\n",
      "Downloading https://files.pythonhosted.org/packages/b2/55/6681ac2cc8de9bb612b1a777606e5beef240bf63aaa6cb03f44af5f42a77/kiwisolver-1.3.1-cp37-cp37m-win_amd64.whl#sha256=8be8d84b7d4f2ba4ffff3665bcd0211318aa632395a1a41553250484a871d454\n",
      "Best match: kiwisolver 1.3.1\n",
      "Processing kiwisolver-1.3.1-cp37-cp37m-win_amd64.whl\n",
      "Installing kiwisolver-1.3.1-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "Adding kiwisolver 1.3.1 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\kiwisolver-1.3.1-py3.7-win-amd64.egg\n",
      "Searching for cycler>=0.10.0\n",
      "Reading https://pypi.org/simple/cycler/\n",
      "Downloading https://files.pythonhosted.org/packages/f7/d2/e07d3ebb2bd7af696440ce7e754c59dd546ffe1bbe732c8ab68b9c834e61/cycler-0.10.0-py2.py3-none-any.whl#sha256=1d8a5ae1ff6c5cf9b93e8811e581232ad8920aeec647c37316ceac982b08cb2d\n",
      "Best match: cycler 0.10.0\n",
      "Processing cycler-0.10.0-py2.py3-none-any.whl\n",
      "Installing cycler-0.10.0-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\cycler-0.10.0-py3.7.egg\\EGG-INFO\\requires.txt\n",
      "Adding cycler 0.10.0 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\cycler-0.10.0-py3.7.egg\n",
      "Searching for typing-extensions<3.8.0,>=3.7.0\n",
      "Reading https://pypi.org/simple/typing-extensions/\n",
      "Downloading https://files.pythonhosted.org/packages/60/7a/e881b5abb54db0e6e671ab088d079c57ce54e8a01a3ca443f561ccadb37e/typing_extensions-3.7.4.3-py3-none-any.whl#sha256=7cb407020f00f7bfc3cb3e7881628838e69d8f3fcab2f64742a5e76b2f841918\n",
      "Best match: typing-extensions 3.7.4.3\n",
      "Processing typing_extensions-3.7.4.3-py3-none-any.whl\n",
      "Installing typing_extensions-3.7.4.3-py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "Adding typing-extensions 3.7.4.3 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\typing_extensions-3.7.4.3-py3.7.egg\n",
      "Searching for requests<3.0.0,>=2.24.0\n",
      "Reading https://pypi.org/simple/requests/\n",
      "Downloading https://files.pythonhosted.org/packages/29/c1/24814557f1d22c56d50280771a17307e6bf87b70727d975fd6b2ce6b014a/requests-2.25.1-py2.py3-none-any.whl#sha256=c210084e36a42ae6b9219e00e48287def368a26d03a048ddad7bfee44f75871e\n",
      "Best match: requests 2.25.1\n",
      "Processing requests-2.25.1-py2.py3-none-any.whl\n",
      "Installing requests-2.25.1-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\requests-2.25.1-py3.7.egg\\EGG-INFO\\requires.txt\n",
      "Adding requests 2.25.1 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\requests-2.25.1-py3.7.egg\n",
      "Searching for pymongo<4.0.0,>=3.8.0\n",
      "Reading https://pypi.org/simple/pymongo/\n",
      "Downloading https://files.pythonhosted.org/packages/ec/93/dfcd3f8ef506d94fa87cc1e0a312b06fac0a57aa5d264ab016522dad4c31/pymongo-3.11.3-cp37-cp37m-win_amd64.whl#sha256=5e1341276ce8b7752db9aeac6bbb0cbe82a3f6a6186866bf6b4906d8d328d50b\n",
      "Best match: pymongo 3.11.3\n",
      "Processing pymongo-3.11.3-cp37-cp37m-win_amd64.whl\n",
      "Installing pymongo-3.11.3-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\pymongo-3.11.3-py3.7-win-amd64.egg\\EGG-INFO\\requires.txt\n",
      "Adding pymongo 3.11.3 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\pymongo-3.11.3-py3.7-win-amd64.egg\n",
      "Searching for pydot<2,>=1.2.0\n",
      "Reading https://pypi.org/simple/pydot/\n",
      "Downloading https://files.pythonhosted.org/packages/ea/76/75b1bb82e9bad3e3d656556eaa353d8cd17c4254393b08ec9786ac8ed273/pydot-1.4.2-py2.py3-none-any.whl#sha256=66c98190c65b8d2e2382a441b4c0edfdb4f4c025ef9cb9874de478fb0793a451\n",
      "Best match: pydot 1.4.2\n",
      "Processing pydot-1.4.2-py2.py3-none-any.whl\n",
      "Installing pydot-1.4.2-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\pydot-1.4.2-py3.7.egg\\EGG-INFO\\requires.txt\n",
      "Adding pydot 1.4.2 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\pydot-1.4.2-py3.7.egg\n",
      "Searching for pyarrow<3.0.0,>=0.15.1\n",
      "Reading https://pypi.org/simple/pyarrow/\n",
      "Downloading https://files.pythonhosted.org/packages/d8/c6/f847631e56f4a8143ccceb0c89f927d4e2f32eea22b300acd21b51f22562/pyarrow-2.0.0-cp37-cp37m-win_amd64.whl#sha256=ccff3a72f70ebfcc002bf75f5ad1248065e5c9c14e0dcfa599a438ea221c5658\n",
      "Best match: pyarrow 2.0.0\n",
      "Processing pyarrow-2.0.0-cp37-cp37m-win_amd64.whl\n",
      "Installing pyarrow-2.0.0-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\pyarrow-2.0.0-py3.7-win-amd64.egg\\EGG-INFO\\requires.txt\n",
      "Adding pyarrow 2.0.0 to easy-install.pth file\n",
      "Installing plasma_store-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "Installing plasma_store.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\pyarrow-2.0.0-py3.7-win-amd64.egg\n",
      "Searching for protobuf<4,>=3.12.2\n",
      "Reading https://pypi.org/simple/protobuf/\n",
      "Downloading https://files.pythonhosted.org/packages/2a/45/59dfbd1f70f541a021e679cc084a70a342ca59d3a0d5e9d05cff5a7f2b9e/protobuf-3.15.7-py2.py3-none-any.whl#sha256=22054432b923c0086f9cf1e1c0c52d39bf3c6e31014ea42eec2dabc22ee26d78\n",
      "Best match: protobuf 3.15.7\n",
      "Processing protobuf-3.15.7-py2.py3-none-any.whl\n",
      "Installing protobuf-3.15.7-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n",
      "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\protobuf-3.15.7-py3.7.egg\\EGG-INFO\\requires.txt\n",
      "Adding protobuf 3.15.7 to easy-install.pth file\n",
      "\n",
      "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\protobuf-3.15.7-py3.7.egg\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining file:///D:/YouTube/OD/TFODCourse/Tensorflow/models/research/slim\n",
      "Requirement already satisfied: six in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from slim==0.1) (1.15.0)\n",
      "Requirement already satisfied: tf-slim>=1.1 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from slim==0.1) (1.1.0)\n",
      "Requirement already satisfied: absl-py>=0.2.2 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tf-slim>=1.1->slim==0.1) (0.12.0)\n",
      "Installing collected packages: slim\n",
      "  Attempting uninstall: slim\n",
      "    Found existing installation: slim 0.1\n",
      "    Uninstalling slim-0.1:\n",
      "      Successfully uninstalled slim-0.1\n",
      "  Running setup.py develop for slim\n",
      "Successfully installed slim\n"
     ]
    }
   ],
   "source": [
    "# Install Tensorflow Object Detection \n",
    "if os.name=='posix':  \n",
    "    !apt-get install protobuf-compiler\n",
    "    !cd Tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=. && cp object_detection/packages/tf2/setup.py . && python -m pip install . \n",
    "    \n",
    "if os.name=='nt':\n",
    "    url=\"https://github.com/protocolbuffers/protobuf/releases/download/v3.15.6/protoc-3.15.6-win64.zip\"\n",
    "    wget.download(url)\n",
    "    !move protoc-3.15.6-win64.zip {paths['PROTOC_PATH']}\n",
    "    !cd {paths['PROTOC_PATH']} && tar -xf protoc-3.15.6-win64.zip\n",
    "    os.environ['PATH'] += os.pathsep + os.path.abspath(os.path.join(paths['PROTOC_PATH'], 'bin'))   \n",
    "    !cd Tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=. && copy object_detection\\\\packages\\\\tf2\\\\setup.py setup.py && python setup.py build && python setup.py install\n",
    "    !cd Tensorflow/models/research/slim && pip install -e . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-04-03 11:13:18.258898: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\n",
      "Running tests under Python 3.7.3: D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\\python.exe\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model\n",
      "2021-04-03 11:13:24.537172: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll\n",
      "2021-04-03 11:13:24.563275: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
      "pciBusID: 0000:2b:00.0 name: GeForce RTX 2070 SUPER computeCapability: 7.5\n",
      "coreClock: 1.785GHz coreCount: 40 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s\n",
      "2021-04-03 11:13:24.563310: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\n",
      "2021-04-03 11:13:25.034685: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll\n",
      "2021-04-03 11:13:25.034709: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll\n",
      "2021-04-03 11:13:25.115325: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll\n",
      "2021-04-03 11:13:25.149186: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll\n",
      "2021-04-03 11:13:25.150141: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusolver64_11.dll'; dlerror: cusolver64_11.dll not found\n",
      "2021-04-03 11:13:25.356866: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll\n",
      "2021-04-03 11:13:25.879448: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll\n",
      "2021-04-03 11:13:25.879478: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1766] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2021-04-03 11:13:25.879799: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2021-04-03 11:13:25.880455: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2021-04-03 11:13:25.880465: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      \n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model): 1.8s\n",
      "I0403 11:13:26.103002  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_center_net_model): 1.8s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_center_net_model\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.16s\n",
      "I0403 11:13:26.261001  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.16s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_experimental_model\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n",
      "I0403 11:13:26.261001  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_experimental_model\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.01s\n",
      "I0403 11:13:26.274001  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.01s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.01s\n",
      "I0403 11:13:26.283001  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.01s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.01s\n",
      "I0403 11:13:26.292001  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.01s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.06s\n",
      "I0403 11:13:26.349001  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.06s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.06s\n",
      "I0403 11:13:26.406001  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.06s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.06s\n",
      "I0403 11:13:26.465714  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.06s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.06s\n",
      "I0403 11:13:26.523714  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.06s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.1s\n",
      "I0403 11:13:26.621714  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.1s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.02s\n",
      "I0403 11:13:26.638714  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.02s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n",
      "[ RUN      ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n",
      "I0403 11:13:26.744284  4864 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b0\n",
      "I0403 11:13:26.744284  4864 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 64\n",
      "I0403 11:13:26.744284  4864 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 3\n",
      "I0403 11:13:26.745285  4864 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0403 11:13:26.755285  4864 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0403 11:13:26.755285  4864 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0403 11:13:26.786285  4864 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0403 11:13:26.786285  4864 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0403 11:13:26.864284  4864 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0403 11:13:26.864284  4864 efficientnet_model.py:147] round_filter input=40 output=40\n",
      "I0403 11:13:26.944523  4864 efficientnet_model.py:147] round_filter input=40 output=40\n",
      "I0403 11:13:26.944523  4864 efficientnet_model.py:147] round_filter input=80 output=80\n",
      "I0403 11:13:27.066525  4864 efficientnet_model.py:147] round_filter input=80 output=80\n",
      "I0403 11:13:27.066525  4864 efficientnet_model.py:147] round_filter input=112 output=112\n",
      "I0403 11:13:27.189525  4864 efficientnet_model.py:147] round_filter input=112 output=112\n",
      "I0403 11:13:27.189525  4864 efficientnet_model.py:147] round_filter input=192 output=192\n",
      "I0403 11:13:27.365525  4864 efficientnet_model.py:147] round_filter input=192 output=192\n",
      "I0403 11:13:27.366525  4864 efficientnet_model.py:147] round_filter input=320 output=320\n",
      "I0403 11:13:27.411523  4864 efficientnet_model.py:147] round_filter input=1280 output=1280\n",
      "I0403 11:13:27.435521  4864 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.0, resolution=224, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0403 11:13:27.470155  4864 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b1\n",
      "I0403 11:13:27.470155  4864 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 88\n",
      "I0403 11:13:27.471155  4864 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 4\n",
      "I0403 11:13:27.472155  4864 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0403 11:13:27.480155  4864 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0403 11:13:27.480155  4864 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0403 11:13:27.541155  4864 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0403 11:13:27.541155  4864 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0403 11:13:27.658154  4864 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0403 11:13:27.658154  4864 efficientnet_model.py:147] round_filter input=40 output=40\n",
      "I0403 11:13:27.827155  4864 efficientnet_model.py:147] round_filter input=40 output=40\n",
      "I0403 11:13:27.827155  4864 efficientnet_model.py:147] round_filter input=80 output=80\n",
      "I0403 11:13:27.989430  4864 efficientnet_model.py:147] round_filter input=80 output=80\n",
      "I0403 11:13:27.989430  4864 efficientnet_model.py:147] round_filter input=112 output=112\n",
      "I0403 11:13:28.154430  4864 efficientnet_model.py:147] round_filter input=112 output=112\n",
      "I0403 11:13:28.154430  4864 efficientnet_model.py:147] round_filter input=192 output=192\n",
      "I0403 11:13:28.376430  4864 efficientnet_model.py:147] round_filter input=192 output=192\n",
      "I0403 11:13:28.376430  4864 efficientnet_model.py:147] round_filter input=320 output=320\n",
      "I0403 11:13:28.473672  4864 efficientnet_model.py:147] round_filter input=1280 output=1280\n",
      "I0403 11:13:28.496715  4864 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.1, resolution=240, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0403 11:13:28.537719  4864 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b2\n",
      "I0403 11:13:28.537719  4864 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 112\n",
      "I0403 11:13:28.537719  4864 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 5\n",
      "I0403 11:13:28.538719  4864 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0403 11:13:28.546719  4864 efficientnet_model.py:147] round_filter input=32 output=32\n",
      "I0403 11:13:28.546719  4864 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0403 11:13:28.609718  4864 efficientnet_model.py:147] round_filter input=16 output=16\n",
      "I0403 11:13:28.609718  4864 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0403 11:13:28.732719  4864 efficientnet_model.py:147] round_filter input=24 output=24\n",
      "I0403 11:13:28.732719  4864 efficientnet_model.py:147] round_filter input=40 output=48\n",
      "I0403 11:13:28.861715  4864 efficientnet_model.py:147] round_filter input=40 output=48\n",
      "I0403 11:13:28.861715  4864 efficientnet_model.py:147] round_filter input=80 output=88\n",
      "I0403 11:13:29.032945  4864 efficientnet_model.py:147] round_filter input=80 output=88\n",
      "I0403 11:13:29.032945  4864 efficientnet_model.py:147] round_filter input=112 output=120\n",
      "I0403 11:13:29.207945  4864 efficientnet_model.py:147] round_filter input=112 output=120\n",
      "I0403 11:13:29.208945  4864 efficientnet_model.py:147] round_filter input=192 output=208\n",
      "I0403 11:13:29.516621  4864 efficientnet_model.py:147] round_filter input=192 output=208\n",
      "I0403 11:13:29.516621  4864 efficientnet_model.py:147] round_filter input=320 output=352\n",
      "I0403 11:13:29.615617  4864 efficientnet_model.py:147] round_filter input=1280 output=1408\n",
      "I0403 11:13:29.639617  4864 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.1, depth_coefficient=1.2, resolution=260, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0403 11:13:29.680617  4864 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b3\n",
      "I0403 11:13:29.680617  4864 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 160\n",
      "I0403 11:13:29.680617  4864 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 6\n",
      "I0403 11:13:29.681617  4864 efficientnet_model.py:147] round_filter input=32 output=40\n",
      "I0403 11:13:29.690620  4864 efficientnet_model.py:147] round_filter input=32 output=40\n",
      "I0403 11:13:29.690620  4864 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0403 11:13:29.751616  4864 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0403 11:13:29.751616  4864 efficientnet_model.py:147] round_filter input=24 output=32\n",
      "I0403 11:13:29.870619  4864 efficientnet_model.py:147] round_filter input=24 output=32\n",
      "I0403 11:13:29.870619  4864 efficientnet_model.py:147] round_filter input=40 output=48\n",
      "I0403 11:13:29.991739  4864 efficientnet_model.py:147] round_filter input=40 output=48\n",
      "I0403 11:13:29.991739  4864 efficientnet_model.py:147] round_filter input=80 output=96\n",
      "I0403 11:13:30.196747  4864 efficientnet_model.py:147] round_filter input=80 output=96\n",
      "I0403 11:13:30.196747  4864 efficientnet_model.py:147] round_filter input=112 output=136\n",
      "I0403 11:13:30.409746  4864 efficientnet_model.py:147] round_filter input=112 output=136\n",
      "I0403 11:13:30.409746  4864 efficientnet_model.py:147] round_filter input=192 output=232\n",
      "I0403 11:13:30.688942  4864 efficientnet_model.py:147] round_filter input=192 output=232\n",
      "I0403 11:13:30.688942  4864 efficientnet_model.py:147] round_filter input=320 output=384\n",
      "I0403 11:13:30.791941  4864 efficientnet_model.py:147] round_filter input=1280 output=1536\n",
      "I0403 11:13:30.815942  4864 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.2, depth_coefficient=1.4, resolution=300, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0403 11:13:30.861942  4864 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b4\n",
      "I0403 11:13:30.861942  4864 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 224\n",
      "I0403 11:13:30.861942  4864 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7\n",
      "I0403 11:13:30.862942  4864 efficientnet_model.py:147] round_filter input=32 output=48\n",
      "I0403 11:13:30.870942  4864 efficientnet_model.py:147] round_filter input=32 output=48\n",
      "I0403 11:13:30.870942  4864 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0403 11:13:30.934946  4864 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0403 11:13:30.934946  4864 efficientnet_model.py:147] round_filter input=24 output=32\n",
      "I0403 11:13:31.095081  4864 efficientnet_model.py:147] round_filter input=24 output=32\n",
      "I0403 11:13:31.095081  4864 efficientnet_model.py:147] round_filter input=40 output=56\n",
      "I0403 11:13:31.255081  4864 efficientnet_model.py:147] round_filter input=40 output=56\n",
      "I0403 11:13:31.255081  4864 efficientnet_model.py:147] round_filter input=80 output=112\n",
      "I0403 11:13:31.605649  4864 efficientnet_model.py:147] round_filter input=80 output=112\n",
      "I0403 11:13:31.605649  4864 efficientnet_model.py:147] round_filter input=112 output=160\n",
      "I0403 11:13:31.866196  4864 efficientnet_model.py:147] round_filter input=112 output=160\n",
      "I0403 11:13:31.866196  4864 efficientnet_model.py:147] round_filter input=192 output=272\n",
      "I0403 11:13:32.256984  4864 efficientnet_model.py:147] round_filter input=192 output=272\n",
      "I0403 11:13:32.256984  4864 efficientnet_model.py:147] round_filter input=320 output=448\n",
      "I0403 11:13:32.364983  4864 efficientnet_model.py:147] round_filter input=1280 output=1792\n",
      "I0403 11:13:32.390983  4864 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.4, depth_coefficient=1.8, resolution=380, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0403 11:13:32.442983  4864 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b5\n",
      "I0403 11:13:32.442983  4864 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 288\n",
      "I0403 11:13:32.442983  4864 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7\n",
      "I0403 11:13:32.444984  4864 efficientnet_model.py:147] round_filter input=32 output=48\n",
      "I0403 11:13:32.452983  4864 efficientnet_model.py:147] round_filter input=32 output=48\n",
      "I0403 11:13:32.452983  4864 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0403 11:13:32.547165  4864 efficientnet_model.py:147] round_filter input=16 output=24\n",
      "I0403 11:13:32.548164  4864 efficientnet_model.py:147] round_filter input=24 output=40\n",
      "I0403 11:13:32.745163  4864 efficientnet_model.py:147] round_filter input=24 output=40\n",
      "I0403 11:13:32.745163  4864 efficientnet_model.py:147] round_filter input=40 output=64\n",
      "I0403 11:13:32.946163  4864 efficientnet_model.py:147] round_filter input=40 output=64\n",
      "I0403 11:13:32.946163  4864 efficientnet_model.py:147] round_filter input=80 output=128\n",
      "I0403 11:13:33.234887  4864 efficientnet_model.py:147] round_filter input=80 output=128\n",
      "I0403 11:13:33.234887  4864 efficientnet_model.py:147] round_filter input=112 output=176\n",
      "I0403 11:13:33.636457  4864 efficientnet_model.py:147] round_filter input=112 output=176\n",
      "I0403 11:13:33.636457  4864 efficientnet_model.py:147] round_filter input=192 output=304\n",
      "I0403 11:13:34.092802  4864 efficientnet_model.py:147] round_filter input=192 output=304\n",
      "I0403 11:13:34.092802  4864 efficientnet_model.py:147] round_filter input=320 output=512\n",
      "I0403 11:13:34.278800  4864 efficientnet_model.py:147] round_filter input=1280 output=2048\n",
      "I0403 11:13:34.307799  4864 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.6, depth_coefficient=2.2, resolution=456, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0403 11:13:34.367801  4864 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b6\n",
      "I0403 11:13:34.367801  4864 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384\n",
      "I0403 11:13:34.367801  4864 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8\n",
      "I0403 11:13:34.368801  4864 efficientnet_model.py:147] round_filter input=32 output=56\n",
      "I0403 11:13:34.377801  4864 efficientnet_model.py:147] round_filter input=32 output=56\n",
      "I0403 11:13:34.377801  4864 efficientnet_model.py:147] round_filter input=16 output=32\n",
      "I0403 11:13:34.472801  4864 efficientnet_model.py:147] round_filter input=16 output=32\n",
      "I0403 11:13:34.472801  4864 efficientnet_model.py:147] round_filter input=24 output=40\n",
      "I0403 11:13:34.711953  4864 efficientnet_model.py:147] round_filter input=24 output=40\n",
      "I0403 11:13:34.711953  4864 efficientnet_model.py:147] round_filter input=40 output=72\n",
      "I0403 11:13:34.956957  4864 efficientnet_model.py:147] round_filter input=40 output=72\n",
      "I0403 11:13:34.956957  4864 efficientnet_model.py:147] round_filter input=80 output=144\n",
      "I0403 11:13:35.292506  4864 efficientnet_model.py:147] round_filter input=80 output=144\n",
      "I0403 11:13:35.292506  4864 efficientnet_model.py:147] round_filter input=112 output=200\n",
      "I0403 11:13:35.651663  4864 efficientnet_model.py:147] round_filter input=112 output=200\n",
      "I0403 11:13:35.652663  4864 efficientnet_model.py:147] round_filter input=192 output=344\n",
      "I0403 11:13:36.332050  4864 efficientnet_model.py:147] round_filter input=192 output=344\n",
      "I0403 11:13:36.332050  4864 efficientnet_model.py:147] round_filter input=320 output=576\n",
      "I0403 11:13:36.524049  4864 efficientnet_model.py:147] round_filter input=1280 output=2304\n",
      "I0403 11:13:36.554049  4864 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.8, depth_coefficient=2.6, resolution=528, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "I0403 11:13:36.624295  4864 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b7\n",
      "I0403 11:13:36.624295  4864 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384\n",
      "I0403 11:13:36.624295  4864 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8\n",
      "I0403 11:13:36.625340  4864 efficientnet_model.py:147] round_filter input=32 output=64\n",
      "I0403 11:13:36.634599  4864 efficientnet_model.py:147] round_filter input=32 output=64\n",
      "I0403 11:13:36.634599  4864 efficientnet_model.py:147] round_filter input=16 output=32\n",
      "I0403 11:13:36.760170  4864 efficientnet_model.py:147] round_filter input=16 output=32\n",
      "I0403 11:13:36.760170  4864 efficientnet_model.py:147] round_filter input=24 output=48\n",
      "I0403 11:13:37.042175  4864 efficientnet_model.py:147] round_filter input=24 output=48\n",
      "I0403 11:13:37.042175  4864 efficientnet_model.py:147] round_filter input=40 output=80\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0403 11:13:37.327793  4864 efficientnet_model.py:147] round_filter input=40 output=80\n",
      "I0403 11:13:37.327793  4864 efficientnet_model.py:147] round_filter input=80 output=160\n",
      "I0403 11:13:37.769363  4864 efficientnet_model.py:147] round_filter input=80 output=160\n",
      "I0403 11:13:37.769363  4864 efficientnet_model.py:147] round_filter input=112 output=224\n",
      "I0403 11:13:38.237657  4864 efficientnet_model.py:147] round_filter input=112 output=224\n",
      "I0403 11:13:38.237657  4864 efficientnet_model.py:147] round_filter input=192 output=384\n",
      "I0403 11:13:39.077081  4864 efficientnet_model.py:147] round_filter input=192 output=384\n",
      "I0403 11:13:39.077081  4864 efficientnet_model.py:147] round_filter input=320 output=640\n",
      "I0403 11:13:39.359760  4864 efficientnet_model.py:147] round_filter input=1280 output=2560\n",
      "I0403 11:13:39.393760  4864 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=2.0, depth_coefficient=3.1, resolution=600, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 12.84s\n",
      "I0403 11:13:39.477760  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 12.84s\n",
      "[       OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n",
      "[ RUN      ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s\n",
      "I0403 11:13:39.481760  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update\n",
      "[ RUN      ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s\n",
      "I0403 11:13:39.482760  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold\n",
      "[ RUN      ] ModelBuilderTF2Test.test_invalid_model_config_proto\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s\n",
      "I0403 11:13:39.482760  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_invalid_model_config_proto\n",
      "[ RUN      ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s\n",
      "I0403 11:13:39.483760  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size\n",
      "[ RUN      ] ModelBuilderTF2Test.test_session\n",
      "[  SKIPPED ] ModelBuilderTF2Test.test_session\n",
      "[ RUN      ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s\n",
      "I0403 11:13:39.484760  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor\n",
      "[ RUN      ] ModelBuilderTF2Test.test_unknown_meta_architecture\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n",
      "I0403 11:13:39.485760  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_unknown_meta_architecture\n",
      "[ RUN      ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n",
      "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n",
      "I0403 11:13:39.485760  4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n",
      "[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n",
      "----------------------------------------------------------------------\n",
      "Ran 21 tests in 15.178s\n",
      "\n",
      "OK (skipped=1)\n"
     ]
    }
   ],
   "source": [
    "VERIFICATION_SCRIPT = os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection', 'builders', 'model_builder_tf2_test.py')\n",
    "# Verify Installation\n",
    "!python {VERIFICATION_SCRIPT}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow-2.5.0rc0-py3.7-win-amd64.egg (2.5.0rc0)\n",
      "Requirement already satisfied: absl-py~=0.10 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow) (0.12.0)\n",
      "Collecting astunparse~=1.6.3\n",
      "  Using cached astunparse-1.6.3-py2.py3-none-any.whl (12 kB)\n",
      "Collecting flatbuffers~=1.12.0\n",
      "  Using cached flatbuffers-1.12-py2.py3-none-any.whl (15 kB)\n",
      "Collecting gast==0.4.0\n",
      "  Using cached gast-0.4.0-py3-none-any.whl (9.8 kB)\n",
      "Collecting google-pasta~=0.2\n",
      "  Using cached google_pasta-0.2.0-py3-none-any.whl (57 kB)\n",
      "Collecting grpcio~=1.34.0\n",
      "  Using cached grpcio-1.34.1-cp37-cp37m-win_amd64.whl (2.9 MB)\n",
      "Collecting h5py~=3.1.0\n",
      "  Using cached h5py-3.1.0-cp37-cp37m-win_amd64.whl (2.7 MB)\n",
      "Collecting keras-nightly~=2.5.0.dev\n",
      "  Using cached keras_nightly-2.5.0.dev2021032900-py2.py3-none-any.whl (1.2 MB)\n",
      "Collecting keras-preprocessing~=1.1.2\n",
      "  Using cached Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB)\n",
      "Requirement already satisfied: numpy~=1.19.2 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow) (1.19.5)\n",
      "Collecting opt-einsum~=3.3.0\n",
      "  Using cached opt_einsum-3.3.0-py3-none-any.whl (65 kB)\n",
      "Requirement already satisfied: protobuf>=3.9.2 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow) (3.15.7)\n",
      "Requirement already satisfied: six~=1.15.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow) (1.15.0)\n",
      "Collecting tensorboard~=2.4\n",
      "  Using cached tensorboard-2.4.1-py3-none-any.whl (10.6 MB)\n",
      "Collecting termcolor~=1.1.0\n",
      "  Using cached termcolor-1.1.0-py3-none-any.whl\n",
      "Collecting tf-estimator-nightly==2.5.0.dev2021032501\n",
      "  Using cached tf_estimator_nightly-2.5.0.dev2021032501-py2.py3-none-any.whl (462 kB)\n",
      "Requirement already satisfied: typing-extensions~=3.7.4 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\typing_extensions-3.7.4.3-py3.7.egg (from tensorflow) (3.7.4.3)\n",
      "Requirement already satisfied: wheel~=0.35 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow) (0.36.2)\n",
      "Collecting wrapt~=1.12.1\n",
      "  Using cached wrapt-1.12.1-cp37-cp37m-win_amd64.whl\n",
      "Collecting cached-property\n",
      "  Using cached cached_property-1.5.2-py2.py3-none-any.whl (7.6 kB)\n",
      "Requirement already satisfied: google-auth<2,>=1.6.3 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorboard~=2.4->tensorflow) (1.28.0)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorboard~=2.4->tensorflow) (1.8.0)\n",
      "Collecting google-auth-oauthlib<0.5,>=0.4.1\n",
      "  Using cached google_auth_oauthlib-0.4.4-py2.py3-none-any.whl (18 kB)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\requests-2.25.1-py3.7.egg (from tensorboard~=2.4->tensorflow) (2.25.1)\n",
      "Collecting markdown>=2.6.8\n",
      "  Using cached Markdown-3.3.4-py3-none-any.whl (97 kB)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorboard~=2.4->tensorflow) (54.2.0)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorboard~=2.4->tensorflow) (1.0.1)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow) (4.7.2)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow) (0.2.8)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow) (4.2.1)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow) (1.3.0)\n",
      "Requirement already satisfied: importlib-metadata in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from markdown>=2.6.8->tensorboard~=2.4->tensorflow) (3.10.0)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow) (0.4.8)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow) (2020.12.5)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow) (4.0.0)\n",
      "Requirement already satisfied: idna<3,>=2.5 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow) (2.10)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow) (1.26.4)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow) (3.1.0)\n",
      "Requirement already satisfied: zipp>=0.5 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from importlib-metadata->markdown>=2.6.8->tensorboard~=2.4->tensorflow) (3.4.1)\n",
      "Installing collected packages: markdown, grpcio, google-auth-oauthlib, cached-property, wrapt, tf-estimator-nightly, termcolor, tensorboard, opt-einsum, keras-preprocessing, keras-nightly, h5py, google-pasta, gast, flatbuffers, astunparse\n",
      "Successfully installed astunparse-1.6.3 cached-property-1.5.2 flatbuffers-1.12 gast-0.4.0 google-auth-oauthlib-0.4.4 google-pasta-0.2.0 grpcio-1.34.1 h5py-3.1.0 keras-nightly-2.5.0.dev2021032900 keras-preprocessing-1.1.2 markdown-3.3.4 opt-einsum-3.3.0 tensorboard-2.4.1 termcolor-1.1.0 tf-estimator-nightly-2.5.0.dev2021032501 wrapt-1.12.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "apache-beam 2.28.0 requires crcmod<2.0,>=1.7, which is not installed.\n",
      "apache-beam 2.28.0 requires dill<0.3.2,>=0.3.1.1, which is not installed.\n",
      "apache-beam 2.28.0 requires fastavro<2,>=0.21.4, which is not installed.\n",
      "apache-beam 2.28.0 requires future<1.0.0,>=0.18.2, which is not installed.\n",
      "apache-beam 2.28.0 requires hdfs<3.0.0,>=2.1.0, which is not installed.\n",
      "apache-beam 2.28.0 requires httplib2<0.18.0,>=0.8, which is not installed.\n",
      "apache-beam 2.28.0 requires mock<3.0.0,>=1.0.1, which is not installed.\n",
      "tensorflow-datasets 4.2.0 requires attrs>=18.1.0, which is not installed.\n",
      "tensorflow-datasets 4.2.0 requires dill, which is not installed.\n",
      "tensorflow-datasets 4.2.0 requires future, which is not installed.\n",
      "tensorflow-datasets 4.2.0 requires importlib-resources, which is not installed.\n",
      "tensorflow-datasets 4.2.0 requires promise, which is not installed.\n",
      "tensorflow-datasets 4.2.0 requires tensorflow-metadata, which is not installed.\n",
      "tensorflow-datasets 4.2.0 requires tqdm, which is not installed.\n",
      "apache-beam 2.28.0 requires avro-python3!=1.9.2,<1.10.0,>=1.8.1, but you have avro-python3 1.10.2 which is incompatible.\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found existing installation: protobuf 3.15.7\n",
      "Uninstalling protobuf-3.15.7:\n",
      "  Successfully uninstalled protobuf-3.15.7\n",
      "Found existing installation: matplotlib 3.4.1\n",
      "Uninstalling matplotlib-3.4.1:\n",
      "  Successfully uninstalled matplotlib-3.4.1\n",
      "Collecting protobuf"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "apache-beam 2.28.0 requires crcmod<2.0,>=1.7, which is not installed.\n",
      "apache-beam 2.28.0 requires dill<0.3.2,>=0.3.1.1, which is not installed.\n",
      "apache-beam 2.28.0 requires fastavro<2,>=0.21.4, which is not installed.\n",
      "apache-beam 2.28.0 requires future<1.0.0,>=0.18.2, which is not installed.\n",
      "apache-beam 2.28.0 requires grpcio<2,>=1.29.0, which is not installed.\n",
      "apache-beam 2.28.0 requires hdfs<3.0.0,>=2.1.0, which is not installed.\n",
      "apache-beam 2.28.0 requires httplib2<0.18.0,>=0.8, which is not installed.\n",
      "apache-beam 2.28.0 requires mock<3.0.0,>=1.0.1, which is not installed.\n",
      "tensorflow 2.5.0rc0 requires astunparse~=1.6.3, which is not installed.\n",
      "tensorflow 2.5.0rc0 requires flatbuffers~=1.12.0, which is not installed.\n",
      "tensorflow 2.5.0rc0 requires gast==0.4.0, which is not installed.\n",
      "tensorflow 2.5.0rc0 requires google-pasta~=0.2, which is not installed.\n",
      "tensorflow 2.5.0rc0 requires grpcio~=1.34.0, which is not installed.\n",
      "tensorflow 2.5.0rc0 requires h5py~=3.1.0, which is not installed.\n",
      "tensorflow 2.5.0rc0 requires keras-nightly~=2.5.0.dev, which is not installed.\n",
      "tensorflow 2.5.0rc0 requires keras-preprocessing~=1.1.2, which is not installed.\n",
      "tensorflow 2.5.0rc0 requires opt-einsum~=3.3.0, which is not installed.\n",
      "tensorflow 2.5.0rc0 requires tensorboard~=2.4, which is not installed.\n",
      "tensorflow 2.5.0rc0 requires termcolor~=1.1.0, which is not installed.\n",
      "tensorflow 2.5.0rc0 requires tf-estimator-nightly==2.5.0.dev2021032501, which is not installed.\n",
      "tensorflow 2.5.0rc0 requires wrapt~=1.12.1, which is not installed.\n",
      "tensorflow-datasets 4.2.0 requires attrs>=18.1.0, which is not installed.\n",
      "tensorflow-datasets 4.2.0 requires dill, which is not installed.\n",
      "tensorflow-datasets 4.2.0 requires future, which is not installed.\n",
      "tensorflow-datasets 4.2.0 requires importlib-resources, which is not installed.\n",
      "tensorflow-datasets 4.2.0 requires promise, which is not installed.\n",
      "tensorflow-datasets 4.2.0 requires tensorflow-metadata, which is not installed.\n",
      "tensorflow-datasets 4.2.0 requires termcolor, which is not installed.\n",
      "tensorflow-datasets 4.2.0 requires tqdm, which is not installed.\n",
      "google-cloud-bigquery 2.13.1 requires google-api-core[grpc]<2.0.0dev,>=1.23.0, which is not installed.\n",
      "google-cloud-bigquery 2.13.1 requires google-cloud-core<2.0dev,>=1.4.1, which is not installed.\n",
      "google-cloud-bigquery 2.13.1 requires google-resumable-media<2.0dev,>=0.6.0, which is not installed.\n",
      "google-cloud-bigquery 2.13.1 requires packaging>=14.3, which is not installed.\n",
      "google-cloud-bigquery 2.13.1 requires proto-plus>=1.10.0, which is not installed.\n",
      "apache-beam 2.28.0 requires avro-python3!=1.9.2,<1.10.0,>=1.8.1, but you have avro-python3 1.10.2 which is incompatible.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Downloading protobuf-3.15.7-cp37-cp37m-win_amd64.whl (904 kB)\n",
      "Collecting matplotlib==3.2\n",
      "  Using cached matplotlib-3.2.0-cp37-cp37m-win_amd64.whl (9.2 MB)\n",
      "Requirement already satisfied: cycler>=0.10 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\cycler-0.10.0-py3.7.egg (from matplotlib==3.2) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\kiwisolver-1.3.1-py3.7-win-amd64.egg (from matplotlib==3.2) (1.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from matplotlib==3.2) (2.8.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\pyparsing-3.0.0b2-py3.7.egg (from matplotlib==3.2) (3.0.0b2)\n",
      "Requirement already satisfied: numpy>=1.11 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from matplotlib==3.2) (1.19.5)\n",
      "Requirement already satisfied: six in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from cycler>=0.10->matplotlib==3.2) (1.15.0)\n",
      "Installing collected packages: protobuf, matplotlib\n",
      "Successfully installed matplotlib-3.2.0 protobuf-3.15.7\n"
     ]
    }
   ],
   "source": [
    "!pip uninstall protobuf matplotlib -y\n",
    "!pip install protobuf matplotlib==3.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import object_detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                       Version             Location\n",
      "----------------------------- ------------------- --------------------------------------------------------\n",
      "absl-py                       0.12.0\n",
      "apache-beam                   2.28.0\n",
      "astunparse                    1.6.3\n",
      "avro-python3                  1.10.2\n",
      "backcall                      0.2.0\n",
      "cached-property               1.5.2\n",
      "cachetools                    4.2.1\n",
      "certifi                       2020.12.5\n",
      "chardet                       4.0.0\n",
      "colorama                      0.4.4\n",
      "contextlib2                   0.6.0.post1\n",
      "cycler                        0.10.0\n",
      "cython                        3.0a6\n",
      "dataclasses                   0.8\n",
      "decorator                     5.0.3\n",
      "flatbuffers                   1.12\n",
      "gast                          0.4.0\n",
      "gin-config                    0.4.0\n",
      "google-api-python-client      2.1.0\n",
      "google-auth                   1.28.0\n",
      "google-auth-oauthlib          0.4.4\n",
      "google-cloud-bigquery         2.13.1\n",
      "google-pasta                  0.2.0\n",
      "grpcio                        1.34.1\n",
      "h5py                          3.1.0\n",
      "idna                          2.10\n",
      "importlib-metadata            3.10.0\n",
      "ipykernel                     5.5.3\n",
      "ipython                       7.22.0\n",
      "ipython-genutils              0.2.0\n",
      "jedi                          0.18.0\n",
      "jupyter-client                6.1.12\n",
      "jupyter-core                  4.7.1\n",
      "kaggle                        1.5.12\n",
      "keras-nightly                 2.5.0.dev2021032900\n",
      "Keras-Preprocessing           1.1.2\n",
      "kiwisolver                    1.3.1\n",
      "lvis                          0.5.3\n",
      "lxml                          4.6.3\n",
      "Markdown                      3.3.4\n",
      "matplotlib                    3.2.0\n",
      "numpy                         1.19.5\n",
      "oauth2client                  4.1.3\n",
      "oauthlib                      3.1.0\n",
      "object-detection              0.1\n",
      "opencv-python                 4.5.1.48\n",
      "opencv-python-headless        4.5.1.48\n",
      "opt-einsum                    3.3.0\n",
      "pandas                        1.2.3\n",
      "parso                         0.8.2\n",
      "pickleshare                   0.7.5\n",
      "pillow                        8.2.0\n",
      "pip                           21.0.1\n",
      "prompt-toolkit                3.0.18\n",
      "protobuf                      3.15.7\n",
      "psutil                        5.8.0\n",
      "py-cpuinfo                    7.0.0\n",
      "pyarrow                       2.0.0\n",
      "pyasn1                        0.4.8\n",
      "pyasn1-modules                0.2.8\n",
      "pycocotools                   2.0.2\n",
      "pydot                         1.4.2\n",
      "Pygments                      2.8.1\n",
      "pymongo                       3.11.3\n",
      "pyparsing                     3.0.0b2\n",
      "PyQt5                         5.15.4\n",
      "PyQt5-Qt5                     5.15.2\n",
      "PyQt5-sip                     12.8.1\n",
      "python-dateutil               2.8.1\n",
      "pytz                          2021.1\n",
      "pywin32                       300\n",
      "pyyaml                        5.4.1\n",
      "pyzmq                         22.0.3\n",
      "requests                      2.25.1\n",
      "requests-oauthlib             1.3.0\n",
      "rsa                           4.7.2\n",
      "scipy                         1.6.2\n",
      "sentencepiece                 0.1.95\n",
      "seqeval                       1.2.2\n",
      "setuptools                    54.2.0\n",
      "six                           1.15.0\n",
      "slim                          0.1                 d:\\youtube\\od\\tfodcourse\\tensorflow\\models\\research\\slim\n",
      "tensorboard                   2.4.1\n",
      "tensorboard-plugin-wit        1.8.0\n",
      "tensorflow                    2.5.0rc0\n",
      "tensorflow-addons             0.12.1\n",
      "tensorflow-datasets           4.2.0\n",
      "tensorflow-hub                0.11.0\n",
      "tensorflow-model-optimization 0.5.0\n",
      "termcolor                     1.1.0\n",
      "tf-estimator-nightly          2.5.0.dev2021032501\n",
      "tf-models-official            2.4.0\n",
      "tf-slim                       1.1.0\n",
      "tornado                       6.1\n",
      "traitlets                     5.0.5\n",
      "typing-extensions             3.7.4.3\n",
      "urllib3                       1.26.4\n",
      "wcwidth                       0.2.5\n",
      "Werkzeug                      1.0.1\n",
      "wget                          3.2\n",
      "wheel                         0.36.2\n",
      "wrapt                         1.12.1\n",
      "zipp                          3.4.1\n"
     ]
    }
   ],
   "source": [
    "!pip list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "csofht2npfDE",
    "outputId": "ff5471b2-bed2-43f2-959c-327a706527b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% [............................................] 20515344 / 20515344        1 file(s) moved.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0.data-00000-of-00001\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/checkpoint\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0.index\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/pipeline.config\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/saved_model.pb\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/variables/\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/variables/variables.data-00000-of-00001\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/variables/variables.index\n"
     ]
    }
   ],
   "source": [
    "if os.name =='posix':\n",
    "    !wget {PRETRAINED_MODEL_URL}\n",
    "    !mv {PRETRAINED_MODEL_NAME+'.tar.gz'} {paths['PRETRAINED_MODEL_PATH']}\n",
    "    !cd {paths['PRETRAINED_MODEL_PATH']} && tar -zxvf {PRETRAINED_MODEL_NAME+'.tar.gz'}\n",
    "if os.name == 'nt':\n",
    "    wget.download(PRETRAINED_MODEL_URL)\n",
    "    !move {PRETRAINED_MODEL_NAME+'.tar.gz'} {paths['PRETRAINED_MODEL_PATH']}\n",
    "    !cd {paths['PRETRAINED_MODEL_PATH']} && tar -zxvf {PRETRAINED_MODEL_NAME+'.tar.gz'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M5KJTnkfpfDC"
   },
   "source": [
    "# 2. Create Label Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "p1BVDWo7pfDC"
   },
   "outputs": [],
   "source": [
    "labels = [{'name':'ThumbsUp', 'id':1}, {'name':'ThumbsDown', 'id':2}, {'name':'ThankYou', 'id':3}, {'name':'LiveLong', 'id':4}]\n",
    "\n",
    "with open(files['LABELMAP'], 'w') as f:\n",
    "    for label in labels:\n",
    "        f.write('item { \\n')\n",
    "        f.write('\\tname:\\'{}\\'\\n'.format(label['name']))\n",
    "        f.write('\\tid:{}\\n'.format(label['id']))\n",
    "        f.write('}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C88zyVELpfDC"
   },
   "source": [
    "# 3. Create TF records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kvf5WccwrFGq",
    "outputId": "49902aeb-0bd7-4298-e1a0-5b4a64eb2064"
   },
   "outputs": [],
   "source": [
    "# OPTIONAL IF RUNNING ON COLAB\n",
    "ARCHIVE_FILES = os.path.join(paths['IMAGE_PATH'], 'archive.tar.gz')\n",
    "if os.path.exists(ARCHIVE_FILES):\n",
    "  !tar -zxvf {ARCHIVE_FILES}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KWpb_BVUpfDD",
    "outputId": "56ce2a3f-3933-4ee6-8a9d-d5ec65f7d73c"
   },
   "outputs": [],
   "source": [
    "if not os.path.exists(files['TF_RECORD_SCRIPT']):\n",
    "    !git clone https://github.com/nicknochnack/GenerateTFRecord {paths['SCRIPTS_PATH']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UPFToGZqpfDD",
    "outputId": "0ebb456f-aadc-4a1f-96e6-fbfec1923e1c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully created the TFRecord file: Tensorflow\\workspace\\annotations\\train.record\n",
      "Successfully created the TFRecord file: Tensorflow\\workspace\\annotations\\test.record\n"
     ]
    }
   ],
   "source": [
    "!python {files['TF_RECORD_SCRIPT']} -x {os.path.join(paths['IMAGE_PATH'], 'train')} -l {files['LABELMAP']} -o {os.path.join(paths['ANNOTATION_PATH'], 'train.record')} \n",
    "!python {files['TF_RECORD_SCRIPT']} -x {os.path.join(paths['IMAGE_PATH'], 'test')} -l {files['LABELMAP']} -o {os.path.join(paths['ANNOTATION_PATH'], 'test.record')} "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qT4QU7pLpfDE"
   },
   "source": [
    "# 4. Copy Model Config to Training Folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "cOjuTFbwpfDF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        1 file(s) copied.\n"
     ]
    }
   ],
   "source": [
    "if os.name =='posix':\n",
    "    !cp {os.path.join(paths['PRETRAINED_MODEL_PATH'], PRETRAINED_MODEL_NAME, 'pipeline.config')} {os.path.join(paths['CHECKPOINT_PATH'])}\n",
    "if os.name == 'nt':\n",
    "    !copy {os.path.join(paths['PRETRAINED_MODEL_PATH'], PRETRAINED_MODEL_NAME, 'pipeline.config')} {os.path.join(paths['CHECKPOINT_PATH'])}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ga8gpNslpfDF"
   },
   "source": [
    "# 5. Update Config For Transfer Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "Z9hRrO_ppfDF"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from object_detection.utils import config_util\n",
    "from object_detection.protos import pipeline_pb2\n",
    "from google.protobuf import text_format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "c2A0mn4ipfDF"
   },
   "outputs": [],
   "source": [
    "config = config_util.get_configs_from_pipeline_file(files['PIPELINE_CONFIG'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uQA13-afpfDF",
    "outputId": "907496a4-a39d-4b13-8c2c-e5978ecb1f10"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model': ssd {\n",
       "   num_classes: 4\n",
       "   image_resizer {\n",
       "     fixed_shape_resizer {\n",
       "       height: 320\n",
       "       width: 320\n",
       "     }\n",
       "   }\n",
       "   feature_extractor {\n",
       "     type: \"ssd_mobilenet_v2_fpn_keras\"\n",
       "     depth_multiplier: 1.0\n",
       "     min_depth: 16\n",
       "     conv_hyperparams {\n",
       "       regularizer {\n",
       "         l2_regularizer {\n",
       "           weight: 3.9999998989515007e-05\n",
       "         }\n",
       "       }\n",
       "       initializer {\n",
       "         random_normal_initializer {\n",
       "           mean: 0.0\n",
       "           stddev: 0.009999999776482582\n",
       "         }\n",
       "       }\n",
       "       activation: RELU_6\n",
       "       batch_norm {\n",
       "         decay: 0.996999979019165\n",
       "         scale: true\n",
       "         epsilon: 0.0010000000474974513\n",
       "       }\n",
       "     }\n",
       "     use_depthwise: true\n",
       "     override_base_feature_extractor_hyperparams: true\n",
       "     fpn {\n",
       "       min_level: 3\n",
       "       max_level: 7\n",
       "       additional_layer_depth: 128\n",
       "     }\n",
       "   }\n",
       "   box_coder {\n",
       "     faster_rcnn_box_coder {\n",
       "       y_scale: 10.0\n",
       "       x_scale: 10.0\n",
       "       height_scale: 5.0\n",
       "       width_scale: 5.0\n",
       "     }\n",
       "   }\n",
       "   matcher {\n",
       "     argmax_matcher {\n",
       "       matched_threshold: 0.5\n",
       "       unmatched_threshold: 0.5\n",
       "       ignore_thresholds: false\n",
       "       negatives_lower_than_unmatched: true\n",
       "       force_match_for_each_row: true\n",
       "       use_matmul_gather: true\n",
       "     }\n",
       "   }\n",
       "   similarity_calculator {\n",
       "     iou_similarity {\n",
       "     }\n",
       "   }\n",
       "   box_predictor {\n",
       "     weight_shared_convolutional_box_predictor {\n",
       "       conv_hyperparams {\n",
       "         regularizer {\n",
       "           l2_regularizer {\n",
       "             weight: 3.9999998989515007e-05\n",
       "           }\n",
       "         }\n",
       "         initializer {\n",
       "           random_normal_initializer {\n",
       "             mean: 0.0\n",
       "             stddev: 0.009999999776482582\n",
       "           }\n",
       "         }\n",
       "         activation: RELU_6\n",
       "         batch_norm {\n",
       "           decay: 0.996999979019165\n",
       "           scale: true\n",
       "           epsilon: 0.0010000000474974513\n",
       "         }\n",
       "       }\n",
       "       depth: 128\n",
       "       num_layers_before_predictor: 4\n",
       "       kernel_size: 3\n",
       "       class_prediction_bias_init: -4.599999904632568\n",
       "       share_prediction_tower: true\n",
       "       use_depthwise: true\n",
       "     }\n",
       "   }\n",
       "   anchor_generator {\n",
       "     multiscale_anchor_generator {\n",
       "       min_level: 3\n",
       "       max_level: 7\n",
       "       anchor_scale: 4.0\n",
       "       aspect_ratios: 1.0\n",
       "       aspect_ratios: 2.0\n",
       "       aspect_ratios: 0.5\n",
       "       scales_per_octave: 2\n",
       "     }\n",
       "   }\n",
       "   post_processing {\n",
       "     batch_non_max_suppression {\n",
       "       score_threshold: 9.99999993922529e-09\n",
       "       iou_threshold: 0.6000000238418579\n",
       "       max_detections_per_class: 100\n",
       "       max_total_detections: 100\n",
       "       use_static_shapes: false\n",
       "     }\n",
       "     score_converter: SIGMOID\n",
       "   }\n",
       "   normalize_loss_by_num_matches: true\n",
       "   loss {\n",
       "     localization_loss {\n",
       "       weighted_smooth_l1 {\n",
       "       }\n",
       "     }\n",
       "     classification_loss {\n",
       "       weighted_sigmoid_focal {\n",
       "         gamma: 2.0\n",
       "         alpha: 0.25\n",
       "       }\n",
       "     }\n",
       "     classification_weight: 1.0\n",
       "     localization_weight: 1.0\n",
       "   }\n",
       "   encode_background_as_zeros: true\n",
       "   normalize_loc_loss_by_codesize: true\n",
       "   inplace_batchnorm_update: true\n",
       "   freeze_batchnorm: false\n",
       " },\n",
       " 'train_config': batch_size: 4\n",
       " data_augmentation_options {\n",
       "   random_horizontal_flip {\n",
       "   }\n",
       " }\n",
       " data_augmentation_options {\n",
       "   random_crop_image {\n",
       "     min_object_covered: 0.0\n",
       "     min_aspect_ratio: 0.75\n",
       "     max_aspect_ratio: 3.0\n",
       "     min_area: 0.75\n",
       "     max_area: 1.0\n",
       "     overlap_thresh: 0.0\n",
       "   }\n",
       " }\n",
       " sync_replicas: true\n",
       " optimizer {\n",
       "   momentum_optimizer {\n",
       "     learning_rate {\n",
       "       cosine_decay_learning_rate {\n",
       "         learning_rate_base: 0.07999999821186066\n",
       "         total_steps: 50000\n",
       "         warmup_learning_rate: 0.026666000485420227\n",
       "         warmup_steps: 1000\n",
       "       }\n",
       "     }\n",
       "     momentum_optimizer_value: 0.8999999761581421\n",
       "   }\n",
       "   use_moving_average: false\n",
       " }\n",
       " fine_tune_checkpoint: \"Tensorflow\\\\workspace\\\\pre-trained-models\\\\ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8\\\\checkpoint\\\\ckpt-0\"\n",
       " num_steps: 50000\n",
       " startup_delay_steps: 0.0\n",
       " replicas_to_aggregate: 8\n",
       " max_number_of_boxes: 100\n",
       " unpad_groundtruth_tensors: false\n",
       " fine_tune_checkpoint_type: \"detection\"\n",
       " fine_tune_checkpoint_version: V2,\n",
       " 'train_input_config': label_map_path: \"Tensorflow\\\\workspace\\\\annotations\\\\label_map.pbtxt\"\n",
       " tf_record_input_reader {\n",
       "   input_path: \"Tensorflow\\\\workspace\\\\annotations\\\\train.record\"\n",
       " },\n",
       " 'eval_config': metrics_set: \"coco_detection_metrics\"\n",
       " use_moving_averages: false,\n",
       " 'eval_input_configs': [label_map_path: \"Tensorflow\\\\workspace\\\\annotations\\\\label_map.pbtxt\"\n",
       " shuffle: false\n",
       " num_epochs: 1\n",
       " tf_record_input_reader {\n",
       "   input_path: \"Tensorflow\\\\workspace\\\\annotations\\\\test.record\"\n",
       " }\n",
       " ],\n",
       " 'eval_input_config': label_map_path: \"Tensorflow\\\\workspace\\\\annotations\\\\label_map.pbtxt\"\n",
       " shuffle: false\n",
       " num_epochs: 1\n",
       " tf_record_input_reader {\n",
       "   input_path: \"Tensorflow\\\\workspace\\\\annotations\\\\test.record\"\n",
       " }}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "9vK5lotDpfDF"
   },
   "outputs": [],
   "source": [
    "pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()\n",
    "with tf.io.gfile.GFile(files['PIPELINE_CONFIG'], \"r\") as f:                                                                                                                                                                                                                     \n",
    "    proto_str = f.read()                                                                                                                                                                                                                                          \n",
    "    text_format.Merge(proto_str, pipeline_config)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "rP43Ph0JpfDG"
   },
   "outputs": [],
   "source": [
    "pipeline_config.model.ssd.num_classes = len(labels)\n",
    "pipeline_config.train_config.batch_size = 4\n",
    "pipeline_config.train_config.fine_tune_checkpoint = os.path.join(paths['PRETRAINED_MODEL_PATH'], PRETRAINED_MODEL_NAME, 'checkpoint', 'ckpt-0')\n",
    "pipeline_config.train_config.fine_tune_checkpoint_type = \"detection\"\n",
    "pipeline_config.train_input_reader.label_map_path= files['LABELMAP']\n",
    "pipeline_config.train_input_reader.tf_record_input_reader.input_path[:] = [os.path.join(paths['ANNOTATION_PATH'], 'train.record')]\n",
    "pipeline_config.eval_input_reader[0].label_map_path = files['LABELMAP']\n",
    "pipeline_config.eval_input_reader[0].tf_record_input_reader.input_path[:] = [os.path.join(paths['ANNOTATION_PATH'], 'test.record')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "oJvfgwWqpfDG"
   },
   "outputs": [],
   "source": [
    "config_text = text_format.MessageToString(pipeline_config)                                                                                                                                                                                                        \n",
    "with tf.io.gfile.GFile(files['PIPELINE_CONFIG'], \"wb\") as f:                                                                                                                                                                                                                     \n",
    "    f.write(config_text)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zr3ON7xMpfDG"
   },
   "source": [
    "# 6. Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "B-Y2UQmQpfDG"
   },
   "outputs": [],
   "source": [
    "TRAINING_SCRIPT = os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection', 'model_main_tf2.py')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "jMP2XDfQpfDH"
   },
   "outputs": [],
   "source": [
    "command = \"python {} --model_dir={} --pipeline_config_path={} --num_train_steps=2000\".format(TRAINING_SCRIPT, paths['CHECKPOINT_PATH'],files['PIPELINE_CONFIG'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A4OXXi-ApfDH",
    "outputId": "117a0e83-012b-466e-b7a6-ccaa349ac5ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python Tensorflow\\models\\research\\object_detection\\model_main_tf2.py --model_dir=Tensorflow\\workspace\\models\\my_ssd_mobnet --pipeline_config_path=Tensorflow\\workspace\\models\\my_ssd_mobnet\\pipeline.config --num_train_steps=2000\n"
     ]
    }
   ],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i3ZsJR-qpfDH",
    "outputId": "cabec5e1-45e6-4f2f-d9cf-297d9c1d0225"
   },
   "outputs": [],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4_YRZu7npfDH"
   },
   "source": [
    "# 7. Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "80L7-fdPpfDH"
   },
   "outputs": [],
   "source": [
    "command = \"python {} --model_dir={} --pipeline_config_path={} --checkpoint_dir={}\".format(TRAINING_SCRIPT, paths['CHECKPOINT_PATH'],files['PIPELINE_CONFIG'], paths['CHECKPOINT_PATH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lYsgEPx9pfDH",
    "outputId": "8632d48b-91d2-45d9-bcb8-c1b172bf6eed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python Tensorflow\\models\\research\\object_detection\\model_main_tf2.py --model_dir=Tensorflow\\workspace\\models\\my_ssd_mobnet --pipeline_config_path=Tensorflow\\workspace\\models\\my_ssd_mobnet\\pipeline.config --checkpoint_dir=Tensorflow\\workspace\\models\\my_ssd_mobnet\n"
     ]
    }
   ],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "lqTV2jGBpfDH"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n"
     ]
    }
   ],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "orvRk02UpfDI"
   },
   "source": [
    "# 8. Load Train Model From Checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8TYk4_oIpfDI"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import tensorflow as tf\n",
    "from object_detection.utils import label_map_util\n",
    "from object_detection.utils import visualization_utils as viz_utils\n",
    "from object_detection.builders import model_builder\n",
    "from object_detection.utils import config_util"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tDnQg-cYpfDI"
   },
   "outputs": [],
   "source": [
    "# Load pipeline config and build a detection model\n",
    "configs = config_util.get_configs_from_pipeline_file(files['PIPELINE_CONFIG'])\n",
    "detection_model = model_builder.build(model_config=configs['model'], is_training=False)\n",
    "\n",
    "# Restore checkpoint\n",
    "ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)\n",
    "ckpt.restore(os.path.join(paths['CHECKPOINT_PATH'], 'ckpt-3')).expect_partial()\n",
    "\n",
    "@tf.function\n",
    "def detect_fn(image):\n",
    "    image, shapes = detection_model.preprocess(image)\n",
    "    prediction_dict = detection_model.predict(image, shapes)\n",
    "    detections = detection_model.postprocess(prediction_dict, shapes)\n",
    "    return detections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0EmsmbBZpfDI"
   },
   "source": [
    "# 9. Detect from an Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Y_MKiuZ4pfDI"
   },
   "outputs": [],
   "source": [
    "import cv2 \n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cBDbIhNapfDI"
   },
   "outputs": [],
   "source": [
    "category_index = label_map_util.create_category_index_from_labelmap(files['LABELMAP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Lx3crOhOzITB"
   },
   "outputs": [],
   "source": [
    "IMAGE_PATH = os.path.join(paths['IMAGE_PATH'], 'test', 'hello.b1c12dc2-91c4-11eb-8963-5cf3709bbcc6.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "id": "Tpzn1SMry1yK",
    "outputId": "c392a2c5-10fe-4fc4-9998-a1d4c7db2bd3"
   },
   "outputs": [],
   "source": [
    "img = cv2.imread(IMAGE_PATH)\n",
    "image_np = np.array(img)\n",
    "\n",
    "input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)\n",
    "detections = detect_fn(input_tensor)\n",
    "\n",
    "num_detections = int(detections.pop('num_detections'))\n",
    "detections = {key: value[0, :num_detections].numpy()\n",
    "              for key, value in detections.items()}\n",
    "detections['num_detections'] = num_detections\n",
    "\n",
    "# detection_classes should be ints.\n",
    "detections['detection_classes'] = detections['detection_classes'].astype(np.int64)\n",
    "\n",
    "label_id_offset = 1\n",
    "image_np_with_detections = image_np.copy()\n",
    "\n",
    "viz_utils.visualize_boxes_and_labels_on_image_array(\n",
    "            image_np_with_detections,\n",
    "            detections['detection_boxes'],\n",
    "            detections['detection_classes']+label_id_offset,\n",
    "            detections['detection_scores'],\n",
    "            category_index,\n",
    "            use_normalized_coordinates=True,\n",
    "            max_boxes_to_draw=5,\n",
    "            min_score_thresh=.8,\n",
    "            agnostic_mode=False)\n",
    "\n",
    "plt.imshow(cv2.cvtColor(image_np_with_detections, cv2.COLOR_BGR2RGB))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IsNAaYAo0WVL"
   },
   "source": [
    "# 10. Real Time Detections from your Webcam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip uninstall opencv-python-headless -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "o_grs6OGpfDJ"
   },
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
    "height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "\n",
    "while cap.isOpened(): \n",
    "    ret, frame = cap.read()\n",
    "    image_np = np.array(frame)\n",
    "    \n",
    "    input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)\n",
    "    detections = detect_fn(input_tensor)\n",
    "    \n",
    "    num_detections = int(detections.pop('num_detections'))\n",
    "    detections = {key: value[0, :num_detections].numpy()\n",
    "                  for key, value in detections.items()}\n",
    "    detections['num_detections'] = num_detections\n",
    "\n",
    "    # detection_classes should be ints.\n",
    "    detections['detection_classes'] = detections['detection_classes'].astype(np.int64)\n",
    "\n",
    "    label_id_offset = 1\n",
    "    image_np_with_detections = image_np.copy()\n",
    "\n",
    "    viz_utils.visualize_boxes_and_labels_on_image_array(\n",
    "                image_np_with_detections,\n",
    "                detections['detection_boxes'],\n",
    "                detections['detection_classes']+label_id_offset,\n",
    "                detections['detection_scores'],\n",
    "                category_index,\n",
    "                use_normalized_coordinates=True,\n",
    "                max_boxes_to_draw=5,\n",
    "                min_score_thresh=.8,\n",
    "                agnostic_mode=False)\n",
    "\n",
    "    cv2.imshow('object detection',  cv2.resize(image_np_with_detections, (800, 600)))\n",
    "    \n",
    "    if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "        cap.release()\n",
    "        cv2.destroyAllWindows()\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rzlM4jt0pfDJ"
   },
   "source": [
    "# 10. Freezing the Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "n4olHB2npfDJ"
   },
   "outputs": [],
   "source": [
    "FREEZE_SCRIPT = os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection', 'exporter_main_v2.py ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0AjO93QDpfDJ"
   },
   "outputs": [],
   "source": [
    "command = \"python {} --input_type=image_tensor --pipeline_config_path={} --trained_checkpoint_dir={} --output_directory={}\".format(FREEZE_SCRIPT ,files['PIPELINE_CONFIG'], paths['CHECKPOINT_PATH'], paths['OUTPUT_PATH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "F6Lsp3tCpfDJ",
    "outputId": "c3828529-bf06-4df5-d7f3-145890ec3edd"
   },
   "outputs": [],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1Sw1ULgHpfDJ",
    "outputId": "6fd441e1-9fc9-4889-d072-3395c21e40b6"
   },
   "outputs": [],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wTPmdqaXpfDK"
   },
   "source": [
    "# 11. Conversion to TFJS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gZ6UzY_fpfDK",
    "outputId": "0c84722e-1c2b-4002-d857-80827ade828a",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!pip install tensorflowjs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0oxbVynHpfDK"
   },
   "outputs": [],
   "source": [
    "command = \"tensorflowjs_converter --input_format=tf_saved_model --output_node_names='detection_boxes,detection_classes,detection_features,detection_multiclass_scores,detection_scores,num_detections,raw_detection_boxes,raw_detection_scores' --output_format=tfjs_graph_model --signature_name=serving_default {} {}\".format(os.path.join(paths['OUTPUT_PATH'], 'saved_model'), paths['TFJS_PATH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DB2AGNmJpfDK",
    "outputId": "fbc9f747-f511-47e8-df8f-5ea65cef0374"
   },
   "outputs": [],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K7rfT4-hpfDK",
    "outputId": "532707fd-6feb-4bc6-84a3-325b5d16303c"
   },
   "outputs": [],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "o8_hm-itpfDK"
   },
   "outputs": [],
   "source": [
    "# Test Code: https://github.com/nicknochnack/RealTimeSignLanguageDetectionwithTFJS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VtUw73FHpfDK"
   },
   "source": [
    "# 12. Conversion to TFLite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XviMtewLpfDK"
   },
   "outputs": [],
   "source": [
    "TFLITE_SCRIPT = os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection', 'export_tflite_graph_tf2.py ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "us86cjC4pfDL"
   },
   "outputs": [],
   "source": [
    "command = \"python {} --pipeline_config_path={} --trained_checkpoint_dir={} --output_directory={}\".format(TFLITE_SCRIPT ,files['PIPELINE_CONFIG'], paths['CHECKPOINT_PATH'], paths['TFLITE_PATH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n1r5YO3rpfDL",
    "outputId": "5fcdf7a4-eee2-4365-f1ca-1751968379ea"
   },
   "outputs": [],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I-xWpHN8pfDL",
    "outputId": "7f6bacd8-d077-43b5-c131-5b081fba24a4"
   },
   "outputs": [],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iJfYMbN6pfDL"
   },
   "outputs": [],
   "source": [
    "FROZEN_TFLITE_PATH = os.path.join(paths['TFLITE_PATH'], 'saved_model')\n",
    "TFLITE_MODEL = os.path.join(paths['TFLITE_PATH'], 'saved_model', 'detect.tflite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "command = \"tflite_convert \\\n",
    "--saved_model_dir={} \\\n",
    "--output_file={} \\\n",
    "--input_shapes=1,300,300,3 \\\n",
    "--input_arrays=normalized_input_image_tensor \\\n",
    "--output_arrays='TFLite_Detection_PostProcess','TFLite_Detection_PostProcess:1','TFLite_Detection_PostProcess:2','TFLite_Detection_PostProcess:3' \\\n",
    "--inference_type=FLOAT \\\n",
    "--allow_custom_ops\".format(FROZEN_TFLITE_PATH, TFLITE_MODEL, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E8GwUeoFpfDL",
    "outputId": "fac43ea4-cc85-471b-a362-e994b06fd583"
   },
   "outputs": [],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nbd7gqHMpfDL",
    "outputId": "7c8fe6d5-2415-4641-8548-39d425c202f7"
   },
   "outputs": [],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5NQqZRdA21Uc"
   },
   "source": [
    "# 13. Zip and Export Models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tTVTGCQp2ZJJ"
   },
   "outputs": [],
   "source": [
    "!tar -czf models.tar.gz {paths['CHECKPOINT_PATH']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "whShhB0x3PYJ",
    "outputId": "b773201d-35c9-46a8-b893-4a76bd4d5d97"
   },
   "outputs": [],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "3. Training and Detection.ipynb",
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